Table of Contents1. What is DeepSeek-V3?2. MoE Architecture: How Does DeepSeek-V3 Optimize Inference?3. DeepSeek-V3 Training Optimization: FP8 + Parallel Computing4. How Does DeepSeek-V3 Perform in Inference?5. How to Deploy DeepSeek-V3? (For Enterprises/Developers)FP8 Training (Floating Point 8-bit Training) Explained1. Why FP8 Training?2. FP8 Format vs. Traditional Floating-Point Formats3. Application of FP8 in DeepSeek-V3 Training4. Challenges and Optimizations in FP8 Training5. Future Prospects for FP8 TrainingWhat is DualPipe Parallelism?1. Why DualPipe Parallelism?2. How DualPipe Parallelism Works3. Advantages of DualPipe Parallelism4. DualPipe Parallelism vs. Other Parallel Methods5. Application of DualPipe Parallelism in DeepSeek-V3 Training In the fiercely competitive era of large language models (LLMs), the DeepSeek-AI team has released DeepSeek-V3, a 6.7T parameter Mixture-of-Experts (MoE) language model with 370B activated parameters that outperforms all open-source models in multiple benchmark tests. This article will delve into the architectural innovations, training optimizations, and inference efficiency improvements of DeepSeek-V3 and explore how it challenges GPT-4o and Claude 3.5 in MMLU, math, and coding tasks. DeepSeek-V3 Technical Report PDF Downlod 1. What is DeepSeek-V3? DeepSeek-V3 is the latest large-scale MoE language model developed by DeepSeek-AI, featuring: 671 billion total parameters, with 370 billion parameters activated per token, significantly reducing computational load; Multi-Token Prediction (MTP) to enhance training efficiency and stabilize inference; Aux-Free Load Balancing, addressing the issue of wasted computational resources in MoE; FP8 training combined with DualPipe parallelism, reducing memory usage and improving training efficiency; High-efficiency inference architecture supporting 128K long contexts, suitable for large-scale application scenarios. DeepSeek-V3 vs. GPT-4o Comparison: In multiple open-source LLM evaluations, DeepSeek-V3 surpasses LLaMA 3, Qwen2.5, and even approaches GPT-4o, particularly excelling in math and coding tasks. 2. MoE Architecture: How Does DeepSeek-V3 Optimize Inference? 2.1 DeepSeekMoE Load Balancing DeepSeek-V3 employs an innovative auxiliary-free load balancing strategy: Intelligent dynamic adjustment of expert weights to reduce MoE computational bottlenecks; Avoids traditional MoE load imbalance issues, making computation more efficient; Combined with FP8 training, reducing memory usage and optimizing inference speed. 2.2 Multi-Token Prediction (MTP) Unlike traditional Transformers that predict only the next token, DeepSeek-V3 predicts multiple tokens at once, resulting in: Denser training signals, leading to faster model convergence; Enhanced text generation fluency, especially suitable for coding and math tasks; Speculative Decoding, doubling inference speed. 3. DeepSeek-V3 Training Optimization: FP8 + Parallel Computing DeepSeek-V3's training leverages 2048 H800 GPUs, optimizing efficiency through FP8 training and DualPipe parallelism: FP8 Training: Reduces computational costs and cuts memory requirements by 50%; DualPipe Parallelism: Overlaps computation and communication, improving GPU utilization; InfiniBand high-speed communication, accelerating cross-node parameter synchronization and enhancing large-scale training performance. Summary: DeepSeek-V3 addresses the two core challenges of large model training and inference—high memory usage and low computational efficiency—through FP8 + efficient MoE. 4. How Does DeepSeek-V3 Perform in Inference? DeepSeek-V3 excels in multiple benchmark tests, outperforming all existing open-source models: Benchmark DeepSeek-V3 DeepSeek-V2.5 Qwen2.5-72B Llama-3.1-405B GPT-4o Claude-3.5 MMLU-Pro 75.9 66.2 71.6 73.3 78.0 78.3 GPQA-D 59.1 41.3 49.0 51.1 65.0 16.0 MATH-500 90.2 74.7 80.0 73.8 78.3 50.8 Codeforces 51.6 35.6 24.8 25.3 23.6 38.8 Mathematical Reasoning: Surpasses LLaMA-3 and Qwen, approaching GPT-4o. Code Generation: Outperforms Claude-3.5 and GPT-4o. 5. How to Deploy DeepSeek-V3? (For Enterprises/Developers) 5.1 Deployment Architecture DeepSeek-V3 supports a high-efficiency inference architecture, recommended for deployment with Ray Serve + vLLM: vLLM: For efficient inference, accelerating token parallel computation; Ray Serve: Supports distributed deployment, achieving load balancing across multiple GPUs; FP8 Inference Optimization: Reduces memory usage, increasing throughput; 128K Context: Suitable for long-text generation. 5.2 Production Environment Optimization GPU Requirements: Minimum 8 x A100/H800 GPUs, or use FP8 version on RTX 4090/3090; Distributed Deployment: Combine with Kubernetes + Ray Serve for cross-node scalability; Model Invocation: Supports OpenAI API-compatible format, facilitating integration into business systems. If you're passionate about the AI field and preparing for AWS or Microsoft certification exams, SPOTO have comprehensive and practical study materials ready for you. Whether you're preparing for AWS's Machine Learning certification (MLA-C01), AI Practitioner certification (AIF-C01), or Microsoft's AI-related exams (AI-900, AI-102), the certification materials I offer will help you study efficiently and increase your chances of passing. Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (click this) AWS AIF-C01 study materials (click this) AWS MLS-C01 study materials (click this) Microsoft AI-900 study materials (click this) Microsoft AI-102 study materials (click this) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! FP8 Training (Floating Point 8-bit Training) Explained FP8 (Floating Point 8-bit) is an 8-bit floating-point format used to reduce computational costs and memory usage in large model training while maintaining numerical precision comparable to FP16/BF16. Compared to traditional FP32 (32-bit floating point) and FP16 (16-bit floating point), FP8 further compresses data storage and computational demands, making large model training and inference more efficient. 1. Why FP8 Training? As large language models (LLMs) grow in parameter size (e.g., DeepSeek-V3 with 6.7T parameters), training and inference face the following challenges: Huge Memory Usage: FP32 requires 4 bytes to store a floating-point number, FP16 requires 2 bytes, while FP8 needs only 1 byte, significantly reducing GPU memory requirements, increasing batch size, and minimizing computational overflow. Computational Performance Limitations: Matrix operations (e.g., MatMul and GEMM) dominate computational resources in large model training. FP8 allows computational units to process more data in parallel, increasing throughput. Energy Optimization: Large model training consumes substantial power. FP8 reduces data transfer and computational demands, lowering overall power consumption and improving GPU efficiency. 2. FP8 Format vs. Traditional Floating-Point Formats FP8 is not a single format but has two main variants: E4M3 (Exponent 4-bit, Mantissa 3-bit) Suitable for activations (Activation) 4-bit exponent, 3-bit mantissa, 1-bit sign Smaller representation range but retains more dynamic changes E5M2 (Exponent 5-bit, Mantissa 2-bit) Suitable for weights (Weights) 5-bit exponent, 2-bit mantissa, 1-bit sign Larger representation range but slightly lower precision Comparison Example: Format Exponent Bits Mantissa Bits Representation Range Applicable Scenario FP32 8 23 ±10³⁸ High-precision deep learning FP16 5 10 ±65,504 Conventional deep learning training/inference BF16 8 7 ±3.9 × 10³⁸ More stable computation, lower precision than FP16 FP8 (E4M3) 4 3 ±448 Suitable for activations FP8 (E5M2) 5 2 ±57344 Suitable for weights 3. Application of FP8 in DeepSeek-V3 Training DeepSeek-V3 employs FP8 mixed-precision training to optimize model training efficiency, including: FP8 training for weights and activations, reducing memory usage by over 50%; FP8 computation for matrix multiplication (GEMM), enhancing computational throughput; Mixed FP8+BF16 training, where: Weights use E5M2 Activations use E4M3 Critical gradient calculations remain in BF16 for stability. 4. Challenges and Optimizations in FP8 Training While FP8 training offers significant storage and computational optimizations, it also presents challenges: Numerical Precision Loss: With only 7-8 total storage bits (even fewer than FP16), FP8 may cause gradient overflow, affecting model convergence. Solution: DeepSeek-V3 uses dynamic scaling to normalize FP8 values dynamically, ensuring stable precision. Computational Unit Support: Traditional GPUs (e.g., RTX 30 series) do not support FP8, requiring specialized hardware optimization. Solution: FP8 training requires GPUs supporting NVIDIA Hopper or Ada Lovelace architectures, such as H100, A100, H800. 5. Future Prospects for FP8 Training FP8 training has become a trend in large model optimization and is likely to be widely used in: Ultra-large LLMs (e.g., DeepSeek-V3, Gemini, GPT-5) Efficient model distillation (reducing training costs) Low-power AI computing (improving energy efficiency) High-concurrency AI tasks (reducing inference latency) What is DualPipe Parallelism? DualPipe Parallelism is a computation-communication overlap optimization strategy designed to enhance the efficiency of large-scale distributed training, particularly for MoE (Mixture of Experts) models and ultra-large LLMs (such as DeepSeek-V3). Its core idea is to overlap computation and communication, reducing the idle time of GPUs waiting for data transfer. In traditional distributed training, especially in MoE structures: Each GPU needs to share experts with multiple nodes, compute results, and then exchange data via All-to-All communication. Since computation and communication are executed serially (communication starts only after computation is complete), communication delay becomes a bottleneck, affecting training efficiency. DualPipe Parallelism uses dual pipeline technology to overlap computation and communication, significantly reducing the idle time of GPU resources and improving GPU utilization. 1. Why DualPipe Parallelism? In DeepSeek-V3 training: MoE Structure: Dynamic task allocation across nodes is required, with each GPU potentially handling multiple experts' computations. Traditional All-to-All Communication: Easily leads to communication congestion, especially in training clusters with 1000+ GPUs, where communication time can exceed computation time. DualPipe Parallelism: By overlapping computation and communication, training tasks do not need to wait for communication completion to start the next computation, effectively improving GPU computational efficiency. 2. How DualPipe Parallelism Works DualPipe Parallelism enhances efficiency through three key optimization steps: 2.1 Computation-Communication Pipeline Overlap While computing the current batch of data, simultaneously communicate the previous batch's data. This way, computational tasks do not idle while waiting for data synchronization, and GPU computational resources are fully utilized. 📌 Illustration (Traditional vs. DualPipe): Traditional Approach (Serial Computation and Communication) Compute Batch1 → Transmit Batch1 → Compute Batch2 → Transmit Batch2 → ... DualPipe Approach (Parallel Computation and Communication) Compute Batch1 → Compute Batch2 Transmit Batch1 → Transmit Batch2 DualPipe allows simultaneous computation and communication, avoiding GPU idling. 2.2 Dynamic Expert Routing In MoE structures, some experts may be "hotter" than others (i.e., used by more tokens), leading to uneven GPU computational load. DualPipe employs a dynamic expert routing mechanism to pre-schedule the optimal expert combination during the computation phase, reducing communication pressure. 2.3 Parallel Gradient Synchronization During training, gradients need to be synchronized across different GPUs. Traditional Method: Synchronize all gradients after computing them (serial). DualPipe: Synchronize the previous batch's gradients while computing the next batch's gradients, reducing gradient synchronization wait time. 3. Advantages of DualPipe Parallelism ✅ Reduced Communication Wait Computation and communication overlap, reducing 80%+ communication wait time, enhancing GPU computational efficiency. ✅ Improved GPU Resource Utilization During training, GPUs no longer idle while waiting for data transfer, increasing overall throughput by 20%-30%. ✅ Optimized MoE Computation Specifically designed for Mixture of Experts (MoE), ensuring more balanced expert allocation and reducing the load on hot GPUs. ✅ Reduced Communication Bottlenecks in Distributed Training In training clusters with 2048+ GPUs, reduces 30%+ communication overhead, effectively boosting large-scale LLM training efficiency. 4. DualPipe Parallelism vs. Other Parallel Methods Parallel Method Computation-Communication Overlap Suitable for MoE Suitable for Large-Scale Training Communication Optimization Data Parallelism (DP) ❌ No ✅ Yes ✅ Yes ❌ Requires gradient synchronization Tensor Parallelism (TP) ❌ No ✅ Yes ✅ Yes ❌ Requires extensive communication Expert Parallelism (EP) ❌ No ✅ Yes ✅ Yes ❌ Requires expert load balancing DualPipe Parallelism ✅ Yes ✅ Yes ✅ Yes ✅ Efficient All-to-All communication 📌 Summary: Data Parallelism (DP) and Tensor Parallelism (TP) are suitable for conventional Transformer structures but suffer from high communication overhead in MoE structures, limiting training efficiency. DualPipe Parallelism is a specialized computational optimization for MoE and ultra-large LLMs, maximizing computation-communication overlap and overall training efficiency. 5. Application of DualPipe Parallelism in DeepSeek-V3 Training DeepSeek-V3's training combines DualPipe Parallelism + FP8 mixed-precision training: DualPipe computation-communication overlap optimizes expert load balancing in MoE computations; FP8 low-precision training reduces memory usage and enhances computational throughput; InfiniBand + NVLink with DualPipe parallelism improves cross-node communication efficiency, enabling training on 2048+ GPUs.    

Table of ContentsIntroduction to DeepSeekThe DeepSeek TeamUsing DeepSeek OnlineDeepSeek API: A Developer's GuideConclusion Introduction to DeepSeek DeepSeek is a high-performance, open-source, and lightweight large language model (LLM) developed by DeepSeek Inc. It is designed to be versatile and adaptable to various scenarios, offering a wide range of services such as intelligent conversation, reasoning, AI search, file processing, translation, problem-solving, creative writing, and programming assistance. The latest release of DeepSeek, the R1 full-blooded version, matches the performance of OpenAI's o1 and o3 models but at a lower cost. The DeepSeek Team The team behind DeepSeek is composed of experienced professionals in the field of artificial intelligence: Zhang Jianwei: Founder and CEO, who has served as a professor at the University of Hamburg and is a member of the Hamburg Academy of Sciences. He brings extensive research background and practical experience in AI. Zhang Yu: Co-founder and Chief Scientist, with years of research and practical experience in natural language processing and deep learning. Liu Yu: Co-founder and Chief Technology Officer, focusing on the research and application of AI technologies, driving the technological innovation and productization of DeepSeek. DeepSeek from entry to mastery (Tsinghua University) PDF Downlod Web Registration Visit the Official Website: Open your web browser and enter the official DeepSeek website address: https://www.deepseek.com/. Click on “Register”: On the homepage, click the “Register” or “Sign Up” button in the top right corner. Choose a Registration Method: Email Registration: Enter a valid email address, set a password, click “Send Verification Code,” check your email, and enter the code. Phone Number Registration: Input your phone number, set a password, click “Get Verification Code,” and fill in the code you receive via SMS. Third-Party Account Login: You can also choose to log in directly using third-party accounts such as WeChat, QQ, or Weibo. Complete Registration: Once the verification is successful, your registration is complete, and you can access the DeepSeek interface. Mobile Registration Download and Install: Apple iOS: Open the App Store, search for “Deepseek” in the search box, click the “Get” button, enter your Apple ID password or use fingerprint/face recognition for verification, and wait for the download to complete. Android: Open your app store (such as Huawei AppGallery, Xiaomi App Store, or AppBao), search for “Deepseek” in the search bar, click “Install,” and wait for the installation to complete. Register an Account: Email Registration: Enter your email address, set a password, click “Send Verification Code,” check your email, and enter the code. Phone Number Registration: Input your phone number, set a password, click “Get Verification Code,” and fill in the code you receive via SMS. Third-Party Account Login: If you have accounts on WeChat, QQ, or Weibo, click the corresponding icon to log in with one-click authorization. Complete Personal Information: After successful registration, go to your personal center to complete your profile information, such as avatar and nickname, and set privacy options to protect your information security. Using DeepSeek Online Official Web Version DeepSeek can be easily accessed through its official web platform. Here's how you can get started: Visit the Website: Go to https://chat.deepseek.com/. Start a Conversation: Click on the "Start Conversation" button. Register an Account: If you are a first-time user, you will need to register using your phone number, WeChat, or email. Log In and Choose a Model: After registration, log in and select either the V3 or R1 model to begin chatting. Official Mobile Version DeepSeek also offers a mobile app for convenient access on the go: Download the App: Search for "DeepSeek" in your device's app store and download the app. Installation: Follow the on-screen instructions to install the app. Usage: The mobile version operates similarly to the web version. Activate the R1 model as needed and start using it. Other Platforms DeepSeek is also available on third-party platforms that offer additional features and ease of use: SiliconFlow: A platform providing high-performance GenAI cloud services. Register here. Lambda Chat: A fast and efficient AI chat platform. Visit Lambda Chat. Metaso AI Search: An AI-powered search engine that allows for extended thinking and internet search capabilities. Try Metaso AI Search. If you're passionate about the AI field and preparing for AWS or Microsoft certification exams, SPOTO have comprehensive and practical study materials ready for you. Whether you're preparing for AWS's Machine Learning certification (MLA-C01), AI Practitioner certification (AIF-C01), or Microsoft's AI-related exams (AI-900, AI-102), the certification materials I offer will help you study efficiently and increase your chances of passing. Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (click this) AWS AIF-C01 study materials (click this) AWS MLS-C01 study materials (click this) Microsoft AI-900 study materials (click this) Microsoft AI-102 study materials (click this) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! DeepSeek API: A Developer's Guide Official API Address DeepSeek's API is accessible through the following official platform and documentation: Official Platform: https://www.deepseek.com/ API Documentation: DeepSeek API Documentation API Pricing DeepSeek offers competitive pricing for its API services: Promotional Period: Until February 8, 2025, 24:00, the deepseek-chat model API calls are available at historical prices. Post-Promotion: After the promotional period, the pricing will be 2 yuan per million input tokens and 8 yuan per million output tokens. Model Comparison: DeepSeek-V3: Lower cost and high cost-effectiveness, suitable for everyday conversations and basic tasks. DeepSeek-R1: Higher performance but more expensive, ideal for complex reasoning and advanced features. How to Use the DeepSeek API Step 1: Register and Create an API Key Visit the Official Website: Go to https://www.deepseek.com/ to https://api-docs.deepseek.com/. Register an Account: Click on "Register" in the top right corner and complete the registration process. Create an API Key: Log in to your account, navigate to the "API Keys" section, and click "Create API Key." Save the key securely, as it will not be shown again after creation. Step 2: Make API Requests Install Required Libraries: If using Python, install the OpenAI SDK with the following command: pip install openai Configure API Key and Base URL: from openai import OpenAI client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.deepseek.com") Basic Chat Completion: response = client.chat.completions.create( model="deepseek-chat", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me about yourself."} ] ) print(response.choices[0].message.content) Advanced Reasoning with DeepSeek-R1: response = client.chat.completions.create( model="deepseek-reasoner", messages=[ {"role": "user", "content": "DeepSeek R1 Guide 2025, Registration, Online Login, and API"} ] ) print(response.choices[0].message.content) Step 3: Monitor API Usage Track Usage: Regularly check your API usage on the DeepSeek platform to avoid exceeding rate limits and budget. Set Alerts: Configure alerts to notify you when your usage approaches certain thresholds. Best Practices for Using DeepSeek API Protect Your API Key: Never share your API Key publicly. If you suspect it has been compromised, revoke it and create a new one. Optimize Requests: Minimize the number of tokens in your requests to reduce costs and improve response times. Handle Errors Gracefully: Implement error handling in your code to manage issues like network errors or invalid responses. Conclusion DeepSeek is a powerful and versatile AI platform that offers a wide range of functionalities through its API. By following the steps outlined in this guide, you can register for DeepSeek, obtain an API Key, and start integrating DeepSeek's capabilities into your applications. Remember to monitor your usage and follow best practices to ensure efficient and secure utilization of the API. Happy coding!

Table of ContentsKey Technologies Behind DeepSeek R1Why It MattersFAQs About DeepSeek R1 Reinforcement Learning (RL) Reinforcement learning (RL) is a type of machine learning where AI learns by taking actions and receiving rewards or punishments based on those actions. The goal is to maximize rewards over time. Example: Imagine teaching a robot to play a game. The robot tries different moves, and every time it makes a good move (e.g., scoring a point), it receives a reward (e.g., +1). If it makes a bad move (e.g., losing a point), it gets a punishment (e.g., -1). Over time, the robot learns which moves score the most points and becomes better at playing the game. SFT Fine-Tuning Fine-tuning a model involves taking a pre-trained AI model and making minor adjustments to it to perform better on a specific task. Instead of training the model from scratch, additional data is used to "fine-tune" it for better performance in a particular use case. SFT (Supervised Fine-Tuning) is a specific type of fine-tuning where the model is trained on a labeled dataset. This means providing the model with examples that include input data (such as images or text) and the correct answers (labels). The model learns to make predictions based on these labeled examples to improve its accuracy for a specific task. Example: Fine-tuning a large language model (LLM) using a labeled dataset of customer support questions and answers to make it more accurate in handling common queries. This is suitable if you have a large amount of labeled data. Knowledge Distillation Model distillation is a method of transferring knowledge from a large, complex model (the "teacher model") to a smaller, simpler model (the "student model"). The goal is to develop a more compact model that retains most of the performance of the larger model while improving efficiency in terms of computational power, memory usage, and inference speed. Cold Start Data This is the minimum amount of labeled data used to help the model gain a general understanding of the task. For example, using a simple dataset scraped from a website's FAQ to fine-tune a chatbot to establish a basic understanding. This is useful when you don't have a large amount of labeled data. Multi-Stage Training Training a model in stages, with each stage focusing on specific improvements, such as accuracy or alignment. For example, training a model on general text data and then improving its conversational abilities through reinforcement learning based on user feedback. Rejection Sampling A method where the model generates multiple potential outputs, but only those that meet specific criteria (such as quality or relevance) are selected for further use. For example, after the RL process, the model generates multiple responses but only retains those useful for retraining the model. DeepSeek from entry to mastery (Tsinghua University) PDF Downlod If you're passionate about the AI field and preparing for AWS or Microsoft certification exams, SPOTO have comprehensive and practical study materials ready for you. Whether you're preparing for AWS's Machine Learning certification (MLA-C01), AI Practitioner certification (AIF-C01), or Microsoft's AI-related exams (AI-900, AI-102), the certification materials I offer will help you study efficiently and increase your chances of passing. Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (click this) AWS AIF-C01 study materials (click this) AWS MLS-C01 study materials (click this) Microsoft AI-900 study materials (click this) Microsoft AI-102 study materials (click this) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! Key Technologies Behind DeepSeek R1 Chain of Thought When you ask most AI models a tricky question, they give an answer but don't explain the reasoning behind it. This is a problem. If the answer is wrong, you don't know where it went wrong. Chain of Thought solves this problem. The model doesn't just give an answer but explains its reasoning step by step. If it makes a mistake, you can clearly see where it went wrong. More importantly, the model itself can see where it went wrong. This is not just a debugging tool. It changes the way the model thinks. The act of explaining forces it to slow down and check its work. Even without additional training, it can produce better answers. DeepSeek's paper shows an example with a math problem. The model realized it made a mistake during the solution process and corrected itself. This is novel. Most AI models don't do this. They either get it right or wrong and move on. Reinforcement Learning Most AI training is like going to school: you show the model a problem, give it the correct answer, and repeat. DeepSeek takes a different approach. Its learning is more like that of a baby. Babies don't take instructions. They try, fail, adjust, and try again. Over time, they get better. This is the principle of reinforcement learning. The model explores different ways to answer a question and selects the most effective one. This is how robots learn to walk and how self-driving cars learn to navigate. Now, DeepSeek is using it to improve reasoning. The key idea is Group Relative Policy Optimization (GRPO). GRPO doesn't simply classify answers as right or wrong but compares them to past attempts. If a new answer is better than the old one, the model updates its behavior. This makes learning cheaper. The model doesn't need a lot of labeled data but trains itself by iterating over its own mistakes. This is why DeepSeek R1 keeps improving over time, while OpenAI's 01 model stays the same. With enough training, it could even reach human-level accuracy in reasoning tasks. Distillation Models like DeepSeek have a problem: they are too big. The full version has 671 billion parameters. Running it requires thousands of GPUs and infrastructure only tech giants can afford. This is impractical for most people. The solution is distillation—compressing a huge model into a smaller one without losing too much performance. It's like teaching an apprentice. The large model generates examples, and the small model learns from them. DeepSeek researchers distilled their model into Llama 3 and Qwen. The surprising part? Sometimes the smaller models perform better than the original. This makes AI more accessible. You no longer need a supercomputer; a single GPU can run powerful models. GRPO RL Framework Traditionally, RL used for training LLMs is most successful when combined with labeled data (e.g., PPO RL framework). This RL method uses a critic model, which acts like an "LLM coach," providing feedback on each move to help the model improve. It evaluates the LLM's actions based on labeled data, assesses the likelihood of the model's success (value function), and guides the model's overall strategy. However, this method is limited by the labeled data used to evaluate decisions. If the labeled data is incomplete, biased, or doesn't cover the entire task, the critic can only provide feedback within those limitations and doesn't generalize well. Training Process Here's a brief overview of each training stage and what it does: Step 1: They fine-tune the base model (DeepSeek-V3-Base) using thousands of cold start data points to lay a solid foundation. For reference, compared to the millions or billions of labeled data points typically required for large-scale supervised learning, thousands of cold start data points are a small fraction. Step 2: Apply pure RL (similar to R1-Zero) to improve reasoning capabilities. Step 3: As RL approaches convergence, they use rejection sampling, where the model selects the best examples from the last successful RL run to create its own labeled data (synthetic data). Have you heard rumors about OpenAI using smaller models to generate synthetic data for the O1 model? It's essentially the same idea. Step 4: Merge the new synthetic data with DeepSeek-V3-Base's supervised data in areas such as writing, fact quality assurance, and self-awareness. This step ensures that the model can learn from high-quality outputs and diverse domain-specific knowledge. Step 5: After fine-tuning with the new data, the model undergoes a final RL process in different prompts and scenarios. So why does DeepSeek-R1 use a multi-stage process? Because each step builds on the previous one. Why It Matters DeepSeek combines chain-of-thought reasoning, reinforcement learning, and model distillation to become a powerful tool. It's not just about raw capability. It's about creating models that are accurate, transparent, and easy to use. Chain of thought makes the model's reasoning clear. Reinforcement learning allows it to continuously improve over time. And distillation ensures that these capabilities are accessible to more people, not just those with access to supercomputers. If you're interested in AI, DeepSeek is worth paying attention to. It's not just another incremental improvement. It's a step towards models that can think, learn, and adapt in ways previously unattainable. You don't need to be an AI researcher to see its potential. The technology behind DeepSeek is already being applied in the real world, from coding assistants to scientific research tools. As these models become more accessible, their impact will only grow. The importance of DeepSeek R1 lies not only in what it can do but also in how it does it. Chain of thought makes AI more transparent. Reinforcement learning makes AI more self-improving. FAQs About DeepSeek R1 What is DeepSeek R1? DeepSeek R1 is a new large language model developed by a Chinese research team. It is significant because its performance on complex tasks such as math, coding, and scientific reasoning is comparable to leading models like OpenAI's o1. The model's innovations, especially in the use of reinforcement learning and model distillation, could make AI more efficient and accessible. How does DeepSeek R1 use "chain of thought" prompts? DeepSeek R1 encourages the model to "think out loud" or provide step-by-step reasoning in its responses. For example, when solving a math problem, it shows each step of its process. This method not only makes it easier to identify mistakes but also allows the model to self-assess and improve accuracy by re-prompting or re-evaluating its steps. How does DeepSeek R1 use reinforcement learning? DeepSeek R1 uses reinforcement learning to learn through self-guided exploration, similar to how a baby learns to walk. Instead of being trained with explicit question-answer pairs, it explores its "environment" and optimizes its behavior by maximizing rewards, such as preferring shorter and more efficient methods when solving equations.        

Table of ContentsWhat Are Large Language Models?Differences in Underlying Principles between DeepSeek R1 Lite Lite and GPT-4oIs DeepSeek R1 Lite a Traditional Probabilistic Generation Model?Distillation ModelsDifferences Among DeepSeek Models with Different Parameters (1.5b, 7b, etc.)Summary In the current fervor surrounding DeepSeek, everyone is eager to experience the full capabilities of these large models and enjoy the smooth output they provide. However, it's essential not only to know how to use DeepSeek but also to understand why it is so powerful. Let's explore the secrets behind these two impressive models in a way that even those without a technical background can easily grasp. DeepSeek from entry to mastery (Tsinghua University) PDF Downlod What Are Large Language Models? Before delving into the specifics of DeepSeek-R1 and GPT-4o, let's first understand what large language models are. These models can be thought of as super-intelligent language assistants that, after learning from vast amounts of text data, can understand human language and generate corresponding responses based on your questions or instructions. For example, if you ask, "What's the weather like tomorrow?" or "Write a short essay about travel," they can provide answers. These models are like knowledgeable scholars with a vast amount of information ready to address your queries. DeepSeek-R1 and GPT-4O are two standout performers among many large language models, each with unique capabilities and characteristics. Differences in Underlying Principles between DeepSeek R1 Lite Lite and GPT-4o Model Architecture DeepSeek-R1's Architectural Features DeepSeek-R1 employs some unique architectural designs, with the most critical being the Mixture of Experts (MoE) architecture. To put it simply, the MoE architecture is like a large team of experts, where each expert is a small neural network specializing in different fields. When you pose a question, a "routing" mechanism decides which expert or group of experts should handle it. For example, if you ask a math question, it will be routed to the math expert; if it's a language-related question, it goes to the language expert. The advantage of this approach is that the most suitable expert handles different types of questions, improving efficiency and reducing computational costs. Imagine we have a large number of document classification tasks, with some documents about technology and others about history. The MoE architecture can assign technology-related documents to experts familiar with that field and historical documents to history experts. Just like in a company where professionals are assigned to tasks they excel in, efficiency is greatly enhanced. Moreover, DeepSeek-R1 uses a dynamic routing mechanism to achieve sparse activation. This means that not all experts are activated during each task; only the necessary ones participate, significantly reducing unnecessary computations and saving resources. Additionally, DeepSeek-R1 incorporates a Multi-Head Latent Attention (MLA) mechanism. When processing language, models need to focus on the relationships between different parts of the text. Traditional Transformer architectures face bottlenecks with KV Cache (which can be thought of as a cache for storing key text information), consuming a lot of memory. The MLA mechanism acts like a smart "compression expert," reducing the storage requirements for KV Cache through low-rank joint compression. For example, consider a long story with many characters and plotlines. Traditional methods might require a large amount of space to store the relationship information between these characters and plotlines. The MLA mechanism can cleverly compress this information, reducing storage needs while maintaining an understanding of the story. This makes the model more efficient when handling large volumes of text. GPT-4o's Architectural Features GPT-4o is based on the Transformer architecture, which is widely used in large language models. The core of the Transformer architecture is the multi-head attention mechanism, allowing the model to focus on different parts of the input text simultaneously to better capture semantic and grammatical information. For example, when we read an article, our brains focus on the beginning, middle, and end of the article, as well as the connections between different paragraphs. The multi-head attention mechanism in Transformers mimics this by using multiple "heads" to focus on different parts of the text in parallel and then integrating this information to gain a comprehensive understanding. GPT-4o builds on this foundation by increasing the model's parameter scale and complexity to enhance its ability to handle complex language tasks. Although the exact number of parameters is not publicly disclosed, it is believed to be extremely large. This enables GPT-4o to perform exceptionally well in tasks such as long-text understanding, multi-turn dialogue management, and cross-domain knowledge transfer. For instance, when processing a several-thousand-word academic paper, GPT-4o can effectively understand the core arguments, research methods, and conclusions of the paper and further analyze and discuss based on this information. Summary of Architectural Differences DeepSeek-R1's MoE architecture stands out in terms of efficiency and cost reduction through expert specialization and sparse activation. In contrast, GPT-4o's Transformer-based architecture focuses on enhancing its ability to handle complex language tasks through large-scale parameters and complex multi-head attention mechanisms. DeepSeek-R1 can be likened to an efficient "team of specialized experts," while GPT-4o is more like a knowledgeable and highly capable "super brain." The different architectural designs lead to differences in performance and application scenarios. Training Data and Methods DeepSeek-R1's Data and Training DeepSeek-R1 employs a very meticulous approach to handling training data, using a "three-stage filtering method." First, it uses regular expressions to remove advertisements and repetitive text from the data, much like cleaning up a bookshelf by discarding duplicate books and useless flyers, leaving only useful and clean content. Then, a BERT-style model is used to score the coherence of the remaining text, retaining only the top 30% of high-quality content. This step is akin to selecting excellent articles, where only those with logical coherence and valuable content are kept. Finally, over-sampling is performed on vertical fields such as code and mathematics, increasing the proportion of professional data to 15%. For example, if we were training a chef, we would not only teach them general cooking knowledge but also focus on specialized training for certain dishes to make them a more comprehensive chef. In terms of training methods, DeepSeek-R1 uses supervised fine-tuning (SFT) and reinforcement learning (RLHF). Supervised fine-tuning is like a teacher correcting a student's homework, pointing out what is right and what is wrong, and allowing the student to improve based on this feedback. Reinforcement learning is like letting the student practice continuously and improve their abilities by receiving rewards (such as good grades). By combining these two methods, DeepSeek-R1 can continuously optimize its language understanding and generation capabilities. GPT-4o's Data and Training GPT-4o's training data is diverse, covering a large amount of multi-language text, with a significant proportion of English data. During training, it employs supervised fine-tuning, multi-stage reinforcement learning (RLHF), and multi-modal alignment. Multi-modal alignment is an important feature of GPT-4o because it supports multi-modal inputs (such as text, images, and audio), so it is necessary to align different modalities of data to enable the model to understand the relationships between different forms of information. For example, when inputting an image and a text description of the image, the model needs to be able to correspond the content of the image with the text description and understand their relationship. Multi-stage reinforcement learning allows the model to learn and optimize at different stages based on different tasks and objectives, gradually enhancing its overall capabilities. Summary of Data and Training Differences DeepSeek R1 Lite focuses more on the processing and optimization of Chinese language materials, using meticulous data filtering and over-sampling in professional fields to enhance its capabilities in specific areas. In contrast, GPT-4o's training data is more diverse, and it invests more in multi-modal processing and multi-stage reinforcement learning to improve its performance in complex multi-modal tasks and cross-domain tasks. It's like two students: one focuses on in-depth learning in a specific subject, while the other emphasizes comprehensive development across multiple disciplines, resulting in different capabilities. If you're passionate about the AI field and preparing for AWS or Microsoft certification exams, SPOTO have comprehensive and practical study materials ready for you. Whether you're preparing for AWS's Machine Learning certification (MLA-C01), AI Practitioner certification (AIF-C01), or Microsoft's AI-related exams (AI-900, AI-102), the certification materials I offer will help you study efficiently and increase your chances of passing. Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (click this) AWS AIF-C01 study materials (click this) AWS MLS-C01 study materials (click this) Microsoft AI-900 study materials (click this) Microsoft AI-102 study materials (click this) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! Is DeepSeek R1 Lite a Traditional Probabilistic Generation Model? DeepSeek-R1 is not a traditional probabilistic generation model but a reasoning model based on reinforcement learning; GPT-4o is a typical probabilistic generation model. Below is a detailed comparison of the two in terms of model principles, training methods, generation mechanisms, application scenarios, advantages, and limitations. Differences in Model Principles DeepSeek-R1: It mainly relies on reinforcement learning, optimizing reasoning strategies through a reward mechanism. During training, it uses the Group Relative Policy Optimization (GRPO) framework, combining accuracy and format rewards to enhance reasoning capabilities. For example, in mathematical problem reasoning, even if the exact answer is not known, generating content that conforms to mathematical principles and is logically consistent can earn rewards, guiding the model's learning process. Its reasoning process is similar to human thinking: it first identifies the problem, formulates solution steps, and then executes calculations or searches. It also self-validates during the process, adjusting the reasoning path if errors are detected. GPT-4o: As a probabilistic generation model based on the Transformer architecture, it relies on the multi-head attention mechanism to understand text. It learns from vast amounts of text data, predicting the probability distribution of the next word or character to generate text. When generating, it selects the most probable word or character based on the probability distribution to ensure text coherence and reasonableness. For example, when inputting "The weather today is very," the model will choose from high-probability words (such as "good" or "sunny") based on learned language patterns to continue the sentence. Differences in Training Methods DeepSeek-R1: It uses a multi-stage training process. First, supervised fine-tuning (SFT) is performed using thousands of high-quality examples to fine-tune the base model. For instance, using few-shot prompting with long reasoning chains (CoT) guides the model to generate detailed answers. Next, reinforcement learning is applied using the GRPO framework to enhance reasoning capabilities. Then, rejection sampling is used to collect new training data to further improve general capabilities. Finally, final reinforcement learning is conducted on various tasks to ensure overall performance. GPT-4o: It depends on multi-modal training and large-scale data training. It supports multi-modal inputs such as text, images, and audio and uses multi-modal training to handle complex tasks, such as understanding image content and generating descriptions. It is trained using large-scale, high-quality multi-modal datasets to enhance natural language processing and multi-modal interaction capabilities. It also uses an end-to-end training method to uniformly train different modalities of data. Differences in Generation Mechanisms DeepSeek-R1: The generation of answers is not simply a matter of piecing together words but relies on reinforcement learning and reasoning chains (CoT). For example, in solving a math problem, the model first outputs a detailed reasoning process before providing the answer. The entire process is logical and well-founded. GPT-4o: It generates text based on learned probability distributions. The generated content is coherent, but in complex reasoning tasks, it may not provide explicit and detailed reasoning steps like DeepSeek-R1. For example, when answering a complex scientific question, it may directly provide a conclusive answer, with the reasoning process hidden within the model and not easily visible to the user. Application Scenarios and Advantages DeepSeek-R1: It is suitable for scenarios requiring deep logical reasoning, such as math problem-solving, programming assistance, and scientific research. In mathematics, it can display detailed solution steps to help users understand. In programming, it can analyze code logic based on requirements and offer optimization suggestions. Its strengths lie in powerful reasoning capabilities and explainability, with reasoning processes in answers that facilitate user verification and learning. GPT-4o: It is suitable for multi-modal fusion scenarios, such as image understanding and generation, cross-modal interaction tasks, and natural language processing general scenarios like text creation and question-answering systems. It excels at generating naturally flowing text content. Limitations DeepSeek-R1: Focusing on reasoning, it has limited capabilities in handling multi-modal information and cannot naturally integrate text, images, audio, and other forms of information like GPT-4o. Additionally, in generating open-ended text (such as creative writing), its flexibility may be inferior to that of GPT-4o. GPT-4o: Although it performs well in multi-modal and language generation, its accuracy and explainability in tasks requiring high-precision reasoning are not as good as DeepSeek-R1. Moreover, large-scale training demands substantial data and computational resources, making it costly. Distillation Models Concept of Distillation Models Imagine a highly knowledgeable scholar who has mastered a vast amount of information. Now, a group of students wants to acquire the same level of knowledge, but they cannot learn everything at once. Distillation models are like a special teaching method that allows the scholar to quickly "transmit" the most critical and useful knowledge to the students, enabling them to gain similar capabilities in a shorter time. In the world of large language models, the "scholar" is a large, complex model with many parameters, known as the "teacher model," while the "students" are smaller, simpler models with fewer parameters, known as "student models." The distillation process involves transferring the knowledge acquired by the teacher model to the student model, allowing the student model to achieve similar performance to the teacher model while maintaining a smaller size and consuming fewer resources. Distillation Models in DeepSeek R1 Lite DeepSeek-R1 has a series of models obtained through distillation techniques, such as the 1.5b, 7b, 8b, 14b, 32b, and 70b models, all of which are student models distilled from a larger base model (similar to the teacher model). Take the 671B model of DeepSeek-R1 as an example. It is like the highly knowledgeable "university scholar" with an extremely high parameter count and strong reasoning capabilities, capable of learning and memorizing a vast amount of knowledge and capturing complex language patterns and semantic relationships. The 1.5b, 7b, and other models are the "students." During the distillation process, the 671B teacher model is first trained to achieve high performance in various language tasks. Next, the trained 671B model makes predictions on the training data, generating a special type of "soft labels," which can be thought of as the key points of knowledge summarized by the scholar. Then, these soft labels, along with the original "hard labels" (which can be understood as basic knowledge points), are used to train the 1.5b, 7b, and other student models. These student models learn from the soft labels generated by the teacher model, improving their performance just as students learn from the key points summarized by the scholar. For example, in a text classification task, the teacher model (the 671B model) can accurately determine which category an article belongs to and can "perceive" the subtle semantic features and their connections to the category. During the distillation process, it passes these "perceptions" to the student model (such as the 7b model) in the form of soft labels. The 7b model, by learning these soft labels, can achieve a high accuracy rate in text classification tasks even though it has far fewer parameters than the 671B model. Differences Among DeepSeek Models with Different Parameters (1.5b, 7b, etc.) Meaning of Parameter Scale In large language models, parameter scale is akin to the number of books in a library. The more parameters, the more knowledge the model can learn. For example, with DeepSeek's 1.5b and 7b models, the "b" stands for billions. The 1.5b model has 1.5 billion parameters, while the 7b model has 7 billion parameters. These parameters act as the model's "memory units," storing the language knowledge, semantic relationships, grammatical rules, and other information learned during training. Just as reading more books increases our knowledge and ability to answer questions, models with larger parameter scales can typically handle more complex tasks and generate more accurate and richer responses. Performance Differences Among Models with Different Parameters Language Understanding Capability The 7b model, with its larger parameter count, has a more comprehensive understanding of language. Therefore, it generally outperforms the 1.5b model in language understanding. For example, when encountering sentences with ambiguous meanings or metaphors, the 7b model is more likely to accurately grasp their true intent. For instance, when presented with the sentence "His heart feels like a rabbit in his chest," the 7b model can better understand that it describes a person's nervousness, whereas the 1.5b model might require more context to accurately interpret it. Quality of Generated Content In terms of content generation, the 7b model also has an advantage. It can produce more coherent and logically structured text. For example, if both models are asked to write a short essay on "The Development Trends of Artificial Intelligence," the 7b model might cover multiple aspects such as technological breakthroughs, expansion of application scenarios, and social impacts, with smooth transitions between paragraphs. In contrast, the 1.5b model might fall short in terms of content richness and coherence, perhaps only touching on a few main points and having less natural paragraph connections. Capability in Handling Complex Tasks When faced with complex tasks, the 7b model performs better. For example, in solving multi-step math problems or writing complex code, the 7b model can leverage its more extensive knowledge base and reasoning capabilities to complete the task more accurately. For instance, when asked to write a complex data analysis program, the 7b model is more likely to consider various boundary cases and optimization solutions, generating more efficient and robust code. The 1.5b model, on the other hand, might encounter logical flaws or be unable to handle certain special cases. Differences in Application Scenarios Applicable Scenarios for the 1.5b Model The 1.5b model, with its smaller parameter scale, requires relatively lower computational resources for operation. Therefore, it is more suitable for scenarios that demand real-time responsiveness and have limited computational resources. For example, in mobile voice assistant applications, users expect quick responses and concise answers. The 1.5b model can meet this demand without excessively consuming the phone's memory and processing power, ensuring that other functions of the phone operate normally. Similarly, in lightweight text generation tools, such as simple copywriting assistance software where users need to quickly generate basic text content like short product descriptions or social media posts, the 1.5b model can efficiently complete these simple tasks and enhance creative efficiency. Applicable Scenarios for the 7b Model The 7b model, with its balanced performance, is suitable for everyday use by average users. It is neither as strained as the 1.5b model when dealing with complex content nor as demanding on hardware as larger models. For example, on an online Q&A platform where users pose a variety of questions, the 7b model can understand the questions and provide relatively accurate and detailed answers. In content creation, it can generate richer and more in-depth text, meeting users' needs for higher quality content. For example, when writing blog posts or short stories, the 7b model can provide a better experience due to its balanced parameter scale and performance. Potential Application Scenarios for Larger Parameter Models (e.g., 8b) Models with larger parameters, such as the 8b model, possess stronger performance and a more extensive knowledge base, making them suitable for scenarios with high demands on model performance. For example, in enterprise-level text processing tasks like contract review and professional document generation and analysis, these tasks often require the model to have a high degree of accuracy and the ability to understand complex business logic. The 8b model can better handle long texts, accurately identify key information, and analyze the semantic and logical structure of the text, thereby providing more reliable services to enterprises. In scientific research fields, such as generating medical literature reviews or assisting in academic paper writing, the requirements for understanding professional terminology and complex research content are very high, and larger parameter models can leverage their strengths to generate more professional and academically compliant text content. Differences in Hardware Requirements Hardware Requirements for the 1.5b Model Due to its smaller parameter count, the 1.5b model has relatively low hardware requirements. Generally, a typical home computer can meet its operational needs. For example, a computer equipped with a 4-core CPU, 8GB of memory, and a graphics card with 4GB of video memory (if GPU acceleration is needed) can run the 1.5b model relatively smoothly. Such hardware configurations are common in most households and small office environments, allowing the 1.5b model to be deployed and used on a wide range of devices. Hardware Requirements for the 7b Model With an increased parameter scale, the 7b model also has higher hardware requirements. It is recommended to use a CPU with more than 8 cores, 16GB or more of memory, and a graphics card with 8GB or more of video memory. This is because when running the 7b model, it requires more computational resources to process and store parameter information and perform complex calculations. For example, when the 7b model processes a longer piece of text, it needs more memory to store the text data and intermediate calculation results. At the same time, more powerful CPUs and GPUs are needed to accelerate the computation process to ensure that the model can provide accurate answers within a reasonable timeframe. Hardware Requirements for the 8b Model The hardware requirements for the 8b model are similar to but slightly higher than those for the 7b model. Due to its larger parameter scale, the computational load during task processing is also greater, necessitating more powerful hardware support. A high-performance multi-core CPU may be required, with memory potentially reaching 20GB or higher, and a graphics card with 12GB or more of video memory. Such hardware configurations are typically found in professional workstations or high-performance servers. For example, in a research institution specializing in natural language processing, to run the 8b model for complex text research and experiments, a high-performance hardware environment needs to be set up to ensure the stable and efficient operation of the model. Summary DeepSeek R1 Lite and GPT-4o have many differences in their underlying principles. In terms of model architecture, DeepSeek R1 Lite's mixture of experts architecture and multi-head latent attention mechanism give it unique characteristics in terms of processing efficiency and resource utilization. In contrast, GPT-4o's Transformer-based architecture excels in handling complex language tasks. Regarding training data and methods, DeepSeek R1 Lite focuses on optimizing Chinese language materials and enhancing specific fields, while GPT-4o leverages diverse multi-modal data and multi-stage reinforcement learning to demonstrate advantages across multiple domains. The different parameter models of DeepSeek, such as the 1.5b and 7b models, also have distinct features. Parameter scale determines the model's language understanding, content generation, and task handling capabilities, which in turn affect their application scenarios. The 1.5b model is suitable for scenarios with limited resources and a demand for quick responses; the 7b model offers balanced performance that meets the everyday needs of average users; and larger parameter models play a role in professional fields with high performance requirements. At the same time, the hardware requirements and inference costs of different parameter models increase with the parameter count. We need to choose the appropriate model based on our actual circumstances.

Fortinet certifications are among the most valuable credentials for IT professionals aiming to specialize in network security. With the growing adoption of Fortinet's solutions, particularly FortiGate firewalls, SD-WAN, and cloud security, there is a rising demand for professionals with Fortinet certifications. Whether you're pursuing the foundational Fortinet Certified Fundamentals (FCF) or the expert-level Fortinet Certified Expert (FCX), the journey to certification requires a structured approach and solid preparation. This guide outlines key strategies to help you succeed in your Fortinet certification exams, regardless of your target level. 1. Understand the Certification Levels and Exam Structure Before diving into study materials, it's crucial to understand the Fortinet certification structure. Fortinet offers five main certification levels, each designed to assess your expertise at different stages of your career: Fortinet Certified Expert (FCX): This is the pinnacle of Fortinet knowledge, validating mastery of advanced security concepts and solutions. Fortinet Certified Solution Specialist (FCSS) — Engineer: This level is for those who specialize in deploying and managing complex Fortinet solutions in areas like SD-WAN or cloud security. Fortinet Certified Professional (FCP): Aimed at those who want to deepen their expertise in specific areas like firewalling or secure access. Fortinet Certified Associate (FCA): This level provides practical skills in deploying and managing basic Fortinet security solutions. Fortinet Certified Fundamentals (FCF): This entry-level certification lays the foundation for understanding Fortinet solutions and is ideal for beginners.   Choosing the right certification path is essential. Understand which level suits your current knowledge and career goals, and start with the fundamentals if you're new to Fortinet products. 2. Leverage Fortinet's Official Training Resources Fortinet offers official resources that are aligned with their certification exams. These resources are designed to help you gain in-depth knowledge of Fortinet's solutions, configurations, and troubleshooting practices. Here's where to start: Fortinet's NSE Training Institute: This platform offers free and paid courses, e-learning modules, and instructor-led training sessions. It covers all certification levels, from Fundamentals to Expert. FortiGate Configuration Guides: As most Fortinet certifications test knowledge on FortiGate firewalls, dive into the official configuration and user guides. The FortiOS Handbook is an excellent resource for NSE 4 and higher levels.   Focus on mastering configuration, monitoring, and troubleshooting Fortinet devices in line with your certification path. 3. Set Up a Hands-On Lab Environment Practical experience is crucial for Fortinet exams, especially those at the Professional and Expert levels. Setting up a home lab or using virtual labs is key to practicing configurations and troubleshooting tasks. Ways to create a lab environment: Virtual Appliances (VMs): Fortinet or platforms like SPOTO offer Virtual Appliances for use in virtual environments like VMware or VirtualBox. This allows you to simulate real-world configurations. Fortinet Developer Network (FNDN): Gain access to FortiGate Cloud and other services for learning and practice. Physical Equipment: If possible, work with real FortiGate hardware to experience the practical application of your skills.   Hands-on labs will help you gain confidence in configuring and securing Fortinet devices, which is essential for passing the practical portions of the exams. 4. Familiarize Yourself with the Exam Objectives Fortinet's exams cover specific objectives that you need to understand thoroughly. Each certification level has a blueprint or syllabus, detailing what will be covered in the exam. These objectives provide a clear roadmap for your studies. For example, if you're preparing for NSE 4 (Fortinet Certified Professional), some of the key topics include: Firewall Policy Configuration VPN Setup and Troubleshooting High Availability (HA) Configuration Advanced Routing Security Profiles (IPS, antivirus, web filtering)   Break down your study sessions according to the exam objectives to ensure you're covering everything you need to know. 5. Practice with Real-World Scenarios Fortinet exams are highly practical, especially for NSE 4 and above. These exams often involve scenarios where you need to configure and troubleshoot FortiGate firewalls under time pressure. To prepare, simulate real-world scenarios and practice solving problems as you would in the exam. Configure VPNs, firewall policies, and high availability (HA). Troubleshoot network issues like latency, traffic routing, and security breaches. Master both CLI (Command Line Interface) and GUI configuration techniques.   Incorporate as much hands-on practice as possible to ensure you're well-prepared for real exam conditions. 6. Join the Fortinet Community Fortinet has an active community where you can engage with other professionals, ask questions, and find helpful resources. Being part of these forums can provide insights into difficult topics, exam strategies, and potential issues others have encountered during their certification journey. Fortinet Community Forum: Participate in discussions, exchange tips, and solve problems with others in the community. Reddit and LinkedIn: Join dedicated groups focused on Fortinet certifications and stay updated on the latest exam trends and best practices.   Collaborating with others in the community helps expand your knowledge and gives you the support you need throughout your study process. 7. Focus on Key Topics Based on Exam Level Different certification levels will emphasize different topics. Here's a brief breakdown of the key areas for each level: FCF & FCA (Entry-level): Focus on basic concepts like firewall policies, NAT, and basic security configurations. Understand FortiGate basics and how to deploy simple solutions. FCP (Professional): Dive deeper into VPN configurations, advanced routing (OSPF, BGP), user authentication, and troubleshooting. FCSS (Solution Specialist): Master the deployment and management of complex Fortinet solutions like SD-WAN, cloud security, and FortiManager. FCX (Expert): Focus on advanced troubleshooting, large-scale deployments, and security protocols like IPSec, SSL VPN, and FortiSIEM.   Focusing on the most tested and relevant topics for your certification level ensures a targeted and efficient study plan. 8. Take Practice Exams and Simulations Taking practice exams and using simulation software can help you assess your readiness and understand the exam format. Many third-party providers offer practice exams that mirror the actual test environment. Why practice exams matter: Simulate real test conditions to get comfortable with the format and time constraints. Identify knowledge gaps and areas that need further review. Build confidence by practicing exam-like questions and scenarios.   9. Review Documentation Thoroughly During the exam, you may be allowed to refer to Fortinet's official documentation. Familiarize yourself with the format of these documents so you can quickly locate relevant information during the exam. Essential documentation to review: FortiOS Handbook: A comprehensive guide for configuring and managing FortiGate firewalls. CLI Reference: Essential for command-line configuration during the practical exam. Product Datasheets: Review documentation for FortiGate, FortiManager, and other Fortinet products to understand their advanced capabilities.   10. Stay Consistent and Take Care of Yourself Consistency is key when studying for Fortinet exams. Establish a study schedule and stick to it. Ensure you're giving yourself ample time to review, practice, and rest. Additionally: Take regular breaks to avoid burnout. Get enough sleep before the exam day. Stay hydrated and energized during study sessions.   Conclusion Preparing for a Fortinet certification exam requires a strategic approach, starting with a solid understanding of the certification levels and exam objectives. By utilizing official Fortinet resources, setting up hands-on labs, practicing with real-world scenarios, and engaging with the Fortinet community, you can increase your chances of success. Whether you're aiming for the Fortinet Certified Fundamentals (FCF) or the expert-level Fortinet Certified Expert (FCX), consistent preparation will ensure you're ready to ace your exam with confidence.

Table of ContentsDownload OllamaDownload Deepseek ModelThird-Party UI ClientModel TestingHardware Requirements for Different VersionsConclusion Recently, many users have encountered issues with Deepseek's servers being busy and unable to respond. Besides constantly refreshing and retrying, another solution is to deploy Deepseek on your local computer. This way, you can use it even without an internet connection! DeepSeek from entry to mastery (Tsinghua University) PDF Downlod Download Ollama Website: https://ollama.com/ First, we need to use a software called Ollama. This is a free and open-source platform for running local large language models. It can help you download the Deepseek model to your computer and run it. Ollama supports both Windows and MacOS. You can simply download it from the official website and install it with a few clicks. After installation, open your computer's command prompt (cmd) and type , then press Enter. If you see an output like the one shown below, it means the installation was successful.ollama If you get an error saying the command is not found, check if the environment variable for Ollama's installation directory is configured in your system. If it is already configured but the error persists, simply restart your computer. Download Deepseek Model Next, go to the Ollama official website and click on deepseek-r1. This will take you to the Deepseek model download page. Currently, Deepseek-r1 offers several model sizes: 1.5b, 7b, 8b, 14b, 32b, 70b, and 671b. The number followed by "b" stands for "billion," indicating the number of parameters in the model. For example, 1.5b means 1.5 billion parameters, and 7b means 7 billion parameters. The larger the number of parameters, the higher the quality of the responses you will get. However, larger models require more GPU resources. If your computer does not have an independent graphics card, choose the 1.5b version. If you have an independent graphics card with 4GB or 8GB of memory, you can choose the 7b or 8b version. Once you have decided on the model version, simply copy the corresponding command and paste it into the cmd terminal. Wait for the model to download and run automatically. When you see the "success" prompt, the local version of Deepseek is deployed. However, at this point, you can only use it via the command line interface in the terminal, which is not very user-friendly. Therefore, we need to use a third-party tool to achieve a more conversational interface. If you're passionate about the AI field and preparing for AWS or Microsoft certification exams, SPOTO have comprehensive and practical study materials ready for you. Whether you're preparing for AWS's Machine Learning certification (MLA-C01), AI Practitioner certification (AIF-C01), or Microsoft's AI-related exams (AI-900, AI-102), the certification materials I offer will help you study efficiently and increase your chances of passing. Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (click this) AWS AIF-C01 study materials (click this) AWS MLS-C01 study materials (click this) Microsoft AI-900 study materials (click this) Microsoft AI-102 study materials (click this) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! Third-Party UI Client Website: https://cherry-ai.com/ We recommend using Cherry Studio, a client that supports multiple large model platforms. It can directly connect to the Ollama API to provide a conversational interface for the large language model. First, download and install the software from the official website. After installation, click on the settings in the lower left corner. In the Model Service section, select ollama. Turn on the switch at the top and click the Manage button at the bottom. In the pop-up interface, add the Deepseek model you just downloaded. Then return to the main conversation interface, and you can start chatting with Deepseek. If you have installed multiple Deepseek models, you can switch between them by clicking on the top menu. Model Testing Let's test the quality of the model's responses with a simple question: "A clock chimes six times in 30 seconds. How long does it take to chime 12 times?" The correct answer is 66 seconds. First, let's see the response from the 1.5b model. The response is very quick, but the answer is verbose and incorrect. Next, let's look at the result from the 14b model. The response is concise and correct. It first determines the time interval for each chime and then calculates the total time for 12 chimes. Hardware Requirements for Different Versions 1. Small Models DeepSeek-R1-1.5B CPU: Minimum 4 cores Memory: 8GB+ Storage: 256GB+ (Model file size: approximately 1.5-2GB) GPU: Not required (CPU-only inference) Use Case: Ideal for local testing and development. Can be easily run on a personal computer with Ollama. Estimated Cost: $2,000 - $5,000. This version is quite accessible for most people.     2. Medium Models DeepSeek-R1-7B CPU: 8 cores+ Memory: 16GB+ Storage: 256GB+ (Model file size: approximately 4-5GB) GPU: Recommended with 8GB+ VRAM (e.g., RTX 3070/4060) Use Case: Suitable for local development and testing of moderately complex natural language processing tasks, such as text summarization, translation, and lightweight multi-turn dialogue systems. Estimated Cost: $5,000 - $10,000. This version is still within reach for many individuals.     DeepSeek-R1-8B CPU: 8 cores+ Memory: 16GB+ Storage: 256GB+ (Model file size: approximately 4-5GB) GPU: Recommended with 8GB+ VRAM (e.g., RTX 3070/4060) Use Case: Suitable for tasks requiring higher precision, such as code generation and logical reasoning. Estimated Cost: $5,000 - $10,000. This version is also achievable with some effort.     3. Large Models DeepSeek-R1-14B CPU: 12 cores+ Memory: 32GB+ Storage: 256GB+ GPU: 16GB+ VRAM (e.g., RTX 4090 or A5000) Use Case: Suitable for enterprise-level complex tasks, such as long-text understanding and generation. Estimated Cost: $20,000 - $30,000. This is a bit steep for someone with a $3,000 salary like me.     DeepSeek-R1-32B CPU: 16 cores+ Memory: 64GB+ Storage: 256GB+ GPU: 24GB+ VRAM (e.g., A100 40GB or dual RTX 3090) Use Case: Suitable for high-precision professional tasks, such as pre-processing for multi-modal tasks. These tasks require high-end CPUs and GPUs and are best suited for well-funded enterprises or research institutions. Estimated Cost: $40,000 - $100,000. This is out of my budget.     4. Super-Large Models DeepSeek-R1-70B CPU: 32 cores+ Memory: 128GB+ Storage: 256GB+ GPU: Multi-GPU setup (e.g., 2x A100 80GB or 4x RTX 4090) Use Case: Suitable for high-complexity generation tasks in research institutions or large enterprises. Estimated Cost: $400,000+. This is something for the boss to consider, not me.     DeepSeek-R1-671B CPU: 64 cores+ Memory: 512GB+ Storage: 512GB+ GPU: Multi-node distributed training (e.g., 8x A100/H100) Use Case: Suitable for large-scale AI research or exploration of Artificial General Intelligence (AGI). Estimated Cost: $20,000,000+. This is something for investors to consider, definitely not me.     The Most Powerful Version: DeepSeek-R1-671B The 671B version of DeepSeek-R1 is the most powerful but also the most demanding in terms of hardware. Deploying this version requires: CPU: 64 cores+ Memory: 512GB+ Storage: 512GB+ GPU: Multi-node distributed training with high-end GPUs like 8x A100 or H100 Additional Requirements: High-power supply (1000W+) and advanced cooling systems     This setup is primarily for large-scale AI research institutions or enterprises with substantial budgets. The cost is prohibitive for most individuals and even many businesses. Conclusion From this, we can conclude that the larger the number of parameters in the model, the higher the quality and accuracy of the responses. However, even if you use the 70 billion parameter version, it is still not the official Deepseek r1 model used on the website, which is the 671 billion parameter version. Although the model size is only 400GB, to run this model locally, you would need at least four A100 GPUs with 80GB of memory each. This is impractical for most individuals. Therefore, the significance of running these smaller models locally is more about experimentation and experience. For personal use, the 8b or 32b versions are more than sufficient. They can still function offline and will not encounter server busy issues, which is something the online version cannot match.

The Concept and Characteristics of Prompt Chains Prompt chains are continuous sequences of prompts used to guide AI content generation. By breaking down complex tasks into manageable subtasks, they ensure that the generated content is logically clear and thematically coherent. Essentially, prompt chains are a "meta-prompt" strategy, not only telling the AI "what to do" but more importantly, guiding the AI "how to do it." DeepSeek from entry to mastery (Tsinghua University) PDF Downlod Mechanisms of Prompt Chains in Content Generation Task Decomposition and Integration Break down the complex topic into several main parts and discuss each part individually. Set specific goals and expected outcomes for each subtask. Summarize the key points of each subtask after completion and link them to the overall theme. Use hierarchical structure diagrams or mind maps to illustrate the relationships between the decomposed parts. Combine the results of each part to write a summary that ensures overall coherence. Framework Construction for Thinking Clearly define the core points of the problem and systematically collect relevant information for analysis. List all key concepts and theories related to the topic and systematically organize them. Use logical framework diagrams to show the process of information collection, analysis, and conclusion. For each key concept, write a brief explanation and explain its role in the article. Validate the effectiveness and applicability of the thinking framework through case analysis or practical application. Activation and Association of Knowledge List all key knowledge points related to the [topic] and explain them in detail one by one. Find key knowledge points related to the [problem] from different fields and make creative associations. Use metaphors or analogies to link [complex concepts] with everyday experiences for easier understanding. Use brainstorming techniques to generate multiple possible associations and innovative points. Integrate the newly generated viewpoints or concepts into the existing knowledge system. Guidance and Expansion of Creativity Think about the [problem/theme] from a completely new angle and propose unique insights. Combine concepts from other fields that are unrelated to this and explore their applications in the [topic]. Set up a new scenario and discuss the development of the [problem/theme] in this scenario. Challenge existing conventional views by thinking from the opposite angle and proposing new possibilities. Combine theories from different disciplines to propose an innovative solution. Start from the result and work backward to deduce possible causes and processes. Quality Control and Optimization Conduct self-assessment and quality checks after each step. Use checklists to ensure each part meets the expected goals and quality standards. Set up mid-term checkpoints to evaluate task progress and quality and make adjustments. Request peer or expert reviews of the content and provide feedback. Optimize and refine each part of the article based on feedback. Multi-Modal Information Processing Combine the text description related to [topic] with data to generate a comprehensive analysis report. Create a report that includes images and data visualization based on [topic], detailing the visualization methods. Design a multimedia content that integrates text, images, audio, or video elements to enhance richness. Design an interactive data display scheme that allows readers to interact with the data, detailing the design steps. Link different media forms of content, such as combining text content with image and data visualization. Select appropriate data visualization tools and detail their usage methods to generate visualized content. Combine specific cases with data analysis to generate a multi-modal report. If you're passionate about the AI field and preparing for AWS or Microsoft certification exams, SPOTO have comprehensive and practical study materials ready for you. Whether you're preparing for AWS's Machine Learning certification (MLA-C01), AI Practitioner certification (AIF-C01), or Microsoft's AI-related exams (AI-900, AI-102), the certification materials I offer will help you study efficiently and increase your chances of passing. Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (click this) AWS AIF-C01 study materials (click this) AWS MLS-C01 study materials (click this) Microsoft AI-900 study materials (click this) Microsoft AI-102 study materials (click this) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! Advantages and Challenges of Prompt Chains Category Advantages Challenges Structured Thinking Guide the AI to create content following a preset logic Designing a reasonable logical structure requires experience and skill Content Depth Achieve deeper content exploration through multi-step guidance Control the output depth of each step to avoid redundancy Creativity Stimulation Stimulate the AI's creative thinking from multiple angles Balance creativity and coherence Quality Control Improve content quality through multiple iterations Requires more practice and computational resources Flexible Adjustment Adjust subsequent prompts based on mid-term results in real-time Requires higher judgment and decision-making abilities Design Principles of Prompt Chains Goal Clarity Logical Coherence Gradual Complexity Adaptive Flexibility Diverse Thinking Feedback Integration Mechanism Design of Modular Prompt Chains The design of prompt chains should follow certain principles to ensure their effectiveness and coherence in task execution. These principles provide clear guidance for the construction of prompt chains, helping to systematically organize and guide the decomposition and processing of tasks. Design Model of Prompt Chains To better understand and design prompt chains, the CIRS model (Context, Instruction, Refinement, Synthesis) can be adopted. This model summarizes the four key stages of prompt chain design: Context: Provide background information and task overview Instruction: Give specific instructions Refinement: Modify and refine the initial output Synthesis: Integrate all outputs to form the final outcome Task Decomposition Steps for Prompt Chain Design Task decomposition is a concept derived from problem-solving theory and systems engineering. Applying task decomposition to prompt design essentially simulates the way humans handle complex problems. This method is based on the principles of divide-and-conquer, hierarchical structure theory, and cognitive load theory. Designing prompt chains based on task decomposition involves the following steps: Clarify Overall Goals Identify Main Tasks Refine Subtasks Define Microtasks Design Corresponding Prompts Establish Task Connections Incorporate Feedback Adjustment Mechanisms SPECTRA Task Decomposition Model Segmentation: Divide the large task into independent but related parts Prioritization: Determine the importance and execution order of subtasks Elaboration: Explore the details of each subtask Connection: Establish logical connections between subtasks Temporal Arrangement: Consider the temporal dimension of tasks Resource Allocation: Allocate appropriate attention resources to each subtask Adaptation: Dynamically adjust the task structure based on AI feedback Prompt Chain Design Techniques Based on the SPECTRA Model Segmentation Prompt: "Break down the [overall task description] into 3-5 main components, ensuring each part is relatively independent but related to the overall goal." Prioritization Prompt: "Prioritize the decomposed tasks based on their importance to the overall goal and logical sequence." Elaboration Prompt: "Select the highest priority subtask and further refine it into 2-3 specific action items or small goals." Connection Prompt: "Analyze the relationships between the subtasks, determine how they support each other and the overall goal." Temporal Arrangement Prompt: "Create a rough timeline for each subtask, considering their dependencies and relative completion times." Resource Allocation Prompt: "Assess the complexity of each subtask and assign an 'attention score' (1-10) to guide resource allocation during execution." Adaptation Prompt: "Evaluate the output quality and contribution of each subtask to the overall goal after execution, and adjust the priority or content of subsequent tasks as needed." Cognitive Theoretical Basis for Creative Thinking Expansion The Geneplore model (Generate-Explore Model) suggests that creative thinking involves two main stages: the generation stage (Generate) and the exploration stage (Explore). This theory can be applied to the process of AI content generation to design corresponding prompt strategies. Divergent Thinking Prompt Chain Design (Based on the "IDEA" Framework) Imagine: Encourage thinking beyond the conventional Diverge: Explore multiple possibilities Expand: Deepen and expand initial ideas Alternate: Seek alternative solutions Operational Methods: Use "hypothetical scenario" prompts to stimulate imagination Apply "multi-angle" prompts to explore different perspectives Use "deepening" prompts to expand initial ideas Design "reversal" prompts to find alternative solutions Convergent Thinking Prompt Chain Design (Based on the "FOCUS" Framework) Filter: Evaluate and select the best ideas Optimize: Improve the selected ideas Combine: Integrate multiple ideas Unify: Create a consistent narrative or solution Synthesize: Form a final conclusion Operational Methods: Use "evaluation matrix" prompts for systematic selection Apply "optimization loop" prompts for iterative improvement Design "creative combination" prompts to integrate different concepts Use "narrative structure" prompts to create a unified storyline Apply "synthesis refinement" prompts to form a final viewpoint Cross-Domain Thinking Prompt Chain Design (Based on the "BRIDGE" Framework) Blend: Combine concepts from different fields Reframe: View problems from a new perspective Interconnect: Establish connections between fields Decontextualize: Extract concepts from their original environments Generalize: Identify universal principles Extrapolate: Apply principles to new fields Operational Methods: Use "random input" prompts to introduce cross-domain elements Apply "analogy mapping" prompts to establish connections between fields Design "abstraction" prompts to extract core principles Use "cross-domain application" prompts to explore new application scenarios Integrated Optimization Strategies for Knowledge and Creativity in Prompt Chains Logic Chain (Logic Chain): Ensure the rigor of reasoning and the coherence of arguments Knowledge Chain (Knowledge Chain): Activate and apply relevant domain knowledge Creativity Chain (Creativity Chain): Promote innovative thinking and unique insights Optimization Strategies for Each Chain: Logic Chain: Apply principles of formal logic, construct argument structure diagrams, use logical connectors to strengthen connections Knowledge Chain: Build multi-level knowledge graphs, implement knowledge retrieval and integration, conduct cross-domain knowledge mapping Creativity Chain: Apply creative thinking techniques, implement concept recombination and fusion, conduct context switching and analogy Dynamic Optimization System for the Three Chains: Balanced Assessment Mechanism: Continuously assess the contributions of the three chains to ensure balanced development Adaptive Switching Mechanism: Dynamically switch focus based on task requirements and current output Cross-Strengthening Strategy: Use the strengths of one chain to compensate for the weaknesses of another chain Integration Checkpoints: Regularly comprehensively assess the logic, knowledge depth, and creativity of the output Practical Application of Complex Task Prompt Chain Design Factors to Consider: Task goals, target audience, article type, word count requirements, special requirements Analysis Phase: First, clarify the task goals and key questions Ideation Phase: Focus on innovative thinking and explore multiple solutions Development Phase: Gradually refine ideas and form specific content plans Assessment Phase: Used for reflection and optimization to ensure the generated content meets expected standards and continues to improve Reflection and Improvement Suggestions: Review and quality assessment of AI-generated content can be conducted through the following framework: Content comprehensiveness Depth of argumentation Innovative insights Practical guidance Structural clarity Language expression Interdisciplinary integration Future prospects Progressive deepening Execution Techniques and Precautions: Dynamic adjustment Regular review Interactive improvement Balanced control Overall prompt chain design framework Pragmatic Intent Analysis (PIA): Decoding the Purpose of Content Generation Theoretical Basis of PIA: PIA is based on pragmatics and speech act theory. It analyzes the pragmatic intentions of tasks to set clear goals for AI and proposes the following classifications: Implementation Steps of PIA: Identify the main pragmatic intention: Determine the primary purpose of the task Analyze secondary pragmatic intentions: Identify any auxiliary purposes Assess the strength of pragmatic intentions: Quantify the intensity of each intention Construct a pragmatic intention matrix: Create a matrix of pragmatic intentions and their intensities Pragmatic Intentions and Strengths: Pragmatic Intention Strength (1-10) Explanation Assertive 8 Provide facts and data on climate change Directive 7 Encourage readers to take environmental actions Expressive 6 Express concern about the threat of climate change Commissive 3 Propose suggestions for future actions Declarative 1 Not applicable for this article Task Goal: Write an article on climate change to raise public awareness and promote action. Main Pragmatic Intentions: (1) Assertive (Strength 8): Provide reliable climate change data and scientific findings. (2) Directive (Strength 7): Encourage readers to take specific environmental actions. (3) Expressive (Strength 6): Convey the urgency of the threat posed by climate change. Ensure the article includes: The latest climate data from authoritative sources Explanations of the causes and impacts of climate change At least 5 actionable steps that readers can take immediately Engaging language to inspire environmental awareness among readers Application Example: Assume the need to write an article on "climate change" with the goal of "enhancing public awareness and promoting action": Pragmatic Intentions: Assertive (Assertive) Directive (Directive) Commissive (Commissive) Expressive (Expressive) Declarative (Declarative) Theme Focus Mechanism (TFM): Locking onto Core Content Theoretical Basis of TFM: TFM draws on cognitive linguistics' "prototype theory" and "frame semantics," developing the following techniques: Implementation Steps of TFM: Define the theme prototype: List key characteristics and representative examples of the theme Construct a semantic framework: Create a concept map related to the theme Establish a gradient of importance: Rank related concepts and sub-themes by importance Create theme guiding symbols: Design specific keywords or phrases to maintain thematic focus Application Example: Theme Prototype: Key characteristics: Global warming, extreme weather, sea-level rise, ecosystem changes Representative examples: Melting of the Arctic ice cap, deforestation of the tropical rainforest, coral bleaching Semantic Framework: Gradient of Importance: (1) Scientific evidence of climate change (2) Current and expected impacts (3) Mitigation and adaptation strategies (4) The importance of individual and collective action Theme Guiding Symbols: Main keywords: Climate change, global warming, environmental protection Secondary keywords: Carbon emissions, renewable energy, sustainable development Prototype Construction of the Theme: Identify the core characteristics and typical examples of the theme Semantic Framework Setting: Create a conceptual network related to the theme Establishment of a Gradient of Importance: Set up a hierarchical structure of relevance to the theme Details Enhancement Strategy (DES): Deepening Content Quality Theoretical Basis of DES: DES integrates cognitive narratology and information processing theory, developing the following strategies: Implementation Steps of DES: Identify key concepts: Determine the core ideas that need detailed elaboration Design a detail matrix: Create a multi-dimensional detail requirement for each key concept Build a micro-macro bridge: Design prompts that connect specific examples to abstract concepts Create a sensory description guideline: Design specific sensory description requirements for abstract concepts Develop a data visualization strategy: Plan how to transform data into vivid narratives or visualizations Example of a Key Concept Detail Matrix for Climate Change: Concept Data Case Sensory Description Comparison Data Visualization Concept Data Case Sensory Description Comparison Global Warming Temperature rise of 1.1°C over the past 100 years Melting of the Arctic ice cap Hot summers, unusually warm winters Comparison of average temperatures 100 years ago and now Sea-level rise Sea-level rise by 3.3 mm per year Risk of Maldives islands being submerged Waves hitting former land, salty sea breeze Comparison of coastlines 50 years ago and now Extreme Weather Frequency of strong hurricanes increased by 20% 2022 European heatwave Howling wind, pouring rain, suffocating heat Comparison of normal summers and heatwave weather Cross-Domain Mapping Mechanism (CMM): Stimulating Innovative Thinking Theoretical Basis of CMM: CMM is based on the conceptual metaphor theory in cognitive linguistics and the analogy reasoning methodology in cognitive science: Implementation Steps of CMM: Source Domain Selection: Choose an appropriate source domain for the analogy based on the task Mapping Point Identification: Determine key correspondences between the source and target domains Analogy Generation: Creatively apply concepts from the source domain to the target domain Analogy Refinement: Adjust and optimize the analogy to ensure its appropriateness and novelty Application Example: Task: Write an article exploring modern cybersecurity strategies using the human immune system as a core analogy. (1) Introduction: Briefly introduce the similarities between the human immune system and cybersecurity systems to set the tone for the entire article. (2) Analogy Expansion: a. Compare firewalls and access controls to skin and mucous membranes, explaining how they serve as the first line of defense. b. Describe how intrusion detection systems patrol the network like white blood cells, identifying and responding to threats. c. Explain how signature-based defense is similar to antibodies, rapidly recognizing and neutralizing known threats. d. Compare system isolation and cleanup processes to fever in the human body, both aiming to control the spread of "infection." e. Discuss how threat intelligence databases are akin to immunological memory, enabling faster responses to recurring threats. (3) In-depth Exploration: a. Analyze how the adaptability of the immune system inspires the design of adaptive security systems. b. Explore how the layered defense strategy of the immune system applies to the concept of defense in depth in cybersecurity. c. Discuss how overactive immune responses (e.g., allergies) might correspond to cybersecurity issues (e.g., false positives or overly restrictive measures). (4) Innovative Ideas: a. Propose the concept of "digital vaccines" to enhance system resistance through simulated attacks. b. Discuss the idea of "cyber hygiene" to prevent diseases through personal hygiene practices. c. Explore the concept of "digital symbiosis," akin to beneficial bacteria in the human body, to enhance cybersecurity using benign AI. (5) Challenges and Prospects: a. Analyze the limitations of this analogy, identifying key differences between the human immune system and cybersecurity systems. b. Look ahead to how other characteristics of biological systems might be further applied to enhance cybersecurity. Note: When using analogies, maintain scientific accuracy to avoid oversimplifying complex technical concepts. Ensure the article is both engaging and technically sound. Concept Grafting Strategy (CGS): Creative Fusion Theoretical Basis of CGS: CGS is based on conceptual blending theory in cognitive science, with the following basic structure: Implementation Steps of CGS: Select Input Concepts: Determine the core concepts to be fused Analyze Concept Characteristics: List the key features and attributes of each input concept Identify Commonalities: Find shared features between input concepts Create Fusion Points: Design innovative connection points between concepts Build Fusion Prompts: Create prompts guiding the AI to perform concept grafting Application Example: Task: Attempt to graft the concepts of "social media" and "traditional library" to design an innovative knowledge-sharing platform. (1) Input Concepts: Social Media: Real-time, interactive, personalized, viral spread Traditional Library: Knowledge repository, systematic classification, quiet study, professional guidance (2) Common Features: Information storage and retrieval Linking user groups Knowledge sharing (3) Fusion Points: Real-time knowledge interaction Knowledge depth social network Digital librarian services Personalized learning paths Knowledge Transfer Technology (KTT): Cross-Domain Wisdom Application Theoretical Basis of KTT: KTT is based on transfer learning theory and organizational learning theory in cognitive science, proposing the following key steps: Implementation Steps of KTT: Define the Problem: Clearly define the problem or innovation point in the target domain Identify the Source Domain: Search for other domains that may contain relevant knowledge or methods Knowledge Extraction: Extract key knowledge, skills, or methods from the source domain Similarity Analysis: Analyze structural similarities between the source and target domains Transfer Strategy Design: Develop a strategy for transferring knowledge from the source domain to the target domain Build Transfer Prompts: Create prompts guiding the AI to perform knowledge transfer Application Example: Task: Improve student engagement in an online education platform by transferring knowledge from game design. (1) Problem Definition: Enhance student engagement and motivation in an online education platform. (2) Source Domain: Game Design Key Knowledge: Game mechanics, player psychology, level design, instant feedback systems (3) Knowledge Extraction and Abstraction: Progress visualization Achievement systems Social interaction Personalized challenges Instant feedback (4) Similarity Analysis: Gamers <-> Students Game levels <-> Course units Skill acquisition in games <-> Knowledge acquisition Game social systems <-> Learning communities (5) Transfer Strategy Design: Integrate game-like elements into the learning experience to increase engagement and motivation. Use progress bars and badges to visualize student progress. Create interactive learning modules that mimic game levels. Provide instant feedback on assignments and quizzes to keep students motivated. (6) Build Transfer Prompts: Design prompts that guide the AI to apply game design principles to the online education platform. Example Prompt: "Create an interactive learning module that uses game mechanics to teach [specific subject]. Include progress visualization, achievement systems, and instant feedback to enhance student engagement." Random Combination Mechanism (RCM): Breaking Conventional Thinking Theoretical Basis of RCM: RCM is based on the theories of "forced association" and "creative synthesis" in creative thinking, proposing the following steps: Implementation Steps of RCM: Define the Creative Domain: Clearly define the specific domain or problem that requires innovation. Build a Multi-Element Library: Collect a diverse range of elements related and unrelated to the creative domain. Design a Random Selection Mechanism: Create a system that can randomly select elements. Establish Combination Rules: Define how the randomly selected elements will be combined. Generate Combination Prompts: Create prompts guiding the AI to perform random combinations. Application Example: Task: Design an innovative marketing campaign for a coffee chain store using RCM to stimulate creativity. (1) Element Library Construction: Coffee-related: Bean types, roasting, extraction, flavors Cultural and artistic: Music, painting, dance, literature Technology: AR, VR, AI, IoT Environmental: Sustainability, recycling, carbon neutrality, biodegradability Social: Social media, live streaming, community, interaction (2) Random Selection: Randomly select elements from the element library. (3) Forced Association: Forcefully connect the randomly selected elements to generate new creative concepts. (4) Creative Integration: Combine the elements in a way that produces innovative ideas. (5) Generate Combination Prompts: Example Prompt: "Create a marketing campaign for a coffee chain that combines [randomly selected elements]. Use AR technology to create an interactive coffee tasting experience, incorporating elements of sustainability and social media engagement." Extreme Assumption Strategy (EHS): Breaking Through Thinking Boundaries Theoretical Basis of EHS: EHS draws on the concepts of "reverse thinking" and "hypothetical thinking," developing the following strategies: Implementation Steps of EHS: Identify Conventional Assumptions: List widely accepted assumptions in a specific domain. Generate Extreme Assumptions: Push these assumptions to the extreme or completely reverse them. Build Hypothetical Scenarios: Describe in detail what would happen if the extreme assumptions were true. Explore Impacts: Analyze the potential impacts of the extreme assumptions on various related aspects. Extract Innovative Ideas: Identify possible innovation opportunities from the extreme scenarios. Build Extreme Assumption Prompts: Create prompts guiding the AI to think through extreme assumptions. Application Example: Task: Use EHS to stimulate innovative thinking on the theme of "future education." (1) Conventional Assumptions: Schools are the primary place for learning. Teachers are the main disseminators of knowledge. Learning requires a long-term effort. Exams are the main way to assess learning outcomes. (2) Extreme Reversal: Completely reverse the conventional assumptions. Example: Learning can occur anywhere, not just in schools. Example: Students can learn independently without teachers. Example: Learning can be achieved quickly, not necessarily over a long period. Example: Exams are no longer necessary to assess learning. (3) Hypothetical Scenario Building: Describe in detail what future education might look like under these extreme assumptions. Example: A world where learning is entirely self-directed and personalized, with no traditional schools or teachers. (4) Impact Exploration: Analyze how these extreme scenarios would affect education, society, and individuals. Example: How would the role of teachers change in a world without traditional schools? (5) Innovation Idea Extraction: Identify potential innovation opportunities from these extreme scenarios. Example: Develop new learning platforms that support self-directed learning without the need for traditional educational institutions. (6) Build Extreme Assumption Prompts: Example Prompt: "Imagine a future where learning is entirely self-directed and personalized. Describe how this would change the role of teachers, the structure of educational institutions, and the way knowledge is acquired." Multiple Constraints Strategy (MCS): Stimulating Creative Problem Solving Theoretical Basis of MCS: MCS is based on creative problem-solving theory and the concept of limited thinking in design thinking, proposing the following key steps: Implementation Steps of MCS: Problem Definition: Clearly define the core problem to be solved. List Constraints: Set multiple challenging constraints. Constraint Impact Analysis: Assess the impact of each constraint on problem-solving. Innovative Solution Conception: Find innovative solutions within the constraints. Constraint Restructuring: Redefine or adjust constraints if necessary. Application Example: Task: Use MCS to design an innovative smart home device. (1) Core Problem: Design a multifunctional smart home device. (2) Constraints: The product must not exceed the size of a standard shoebox. It must meet five different home needs simultaneously. The product price must not exceed $100. It must be made from 100% recyclable materials. It must be suitable for all age groups from children to the elderly. (3) Constraint Impact Analysis: Assess how each constraint affects the design and functionality of the device. (4) Innovative Solution Conception: Find creative ways to meet all constraints while fulfilling the core problem. Example: Design a modular smart home device that can be customized to meet different needs within the size and cost constraints. (5) Constraint Restructuring: If necessary, redefine or adjust constraints to make the problem more feasible. Example: Adjust the size constraint slightly to allow for more functionality while still keeping it compact. Stylistic Simulation Mechanism (RSM): Precisely Capturing Language Characteristics Theoretical Basis of RSM: RSM is based on register theory and stylistic analysis in linguistics, with the following steps: Implementation Steps of RSM: Determine the Target Style: Clearly define the specific language style to be simulated. Collect Stylistic Samples: Gather typical text samples of the target style. Analyze Language Features: Analyze the stylistic features from vocabulary, syntax, rhetoric, and other dimensions. Extract Key Elements: Identify and extract unique language elements that constitute the style. Build a Stylistic Guide: Create a detailed guide for using the style. Generate Simulation Prompts: Create prompts guiding the AI to simulate the specific style. Application Example: Task: Guide the AI to generate a short story in the style of Shakespeare. (1) Shakespearean Style Feature Analysis: Vocabulary: Use of Old English words, creative compound words Grammar: Inverted sentences, irregular sentence structures Rhetoric: Extensive use of metaphors, similes, and puns Meter: Iambic pentameter Themes: Common themes such as love, power, betrayal (2) Contextual Factors Consideration: Consider the historical and cultural context of Shakespeare's works. (3) Stylistic Elements Extraction: Identify key elements that define the Shakespearean style. (4) Stylistic Guide Building: Create a guide that outlines how to use these elements in writing. (5) Generate Simulation Prompts: Example Prompt: "Write a short story in the style of Shakespeare. Use Old English vocabulary, inverted sentence structures, and iambic pentameter. Incorporate themes of love and betrayal." Emotional Integration Strategy (EIS): Enhancing Textual Impact Theoretical Basis of EIS: EIS is based on the research findings of emotional linguistics and psycholinguistics, developing the following strategies: Implementation Steps of EIS: Determine the Target Emotion: Clearly define the main emotional tone of the text. Create an Emotional Word Library: Collect words and phrases related to the target emotion. Design an Emotional Curve: Plan the intensity of emotions throughout the text. Select Emotional Trigger Points: Place emotional elements at key points in the text. Build Emotional Scenarios: Create scenarios or details that evoke emotional resonance. Generate Emotional Integration Prompts: Create prompts guiding the AI to integrate emotional elements. Application Example: Task: Guide the AI to generate a short story on the theme of "parting." (1) Emotional Vocabulary Selection: Choose words and phrases that convey sadness and reluctance. (2) Tone Regulation: Ensure the tone is somber and reflective. (3) Imagery Building: Use imagery that evokes feelings of loss and longing. (4) Emotional Rhythm Control: Plan how the emotional intensity will rise and fall throughout the story. (5) Generate Emotional Integration Prompts: Example Prompt: "Write a short story on the theme of parting. Use words that convey sadness and reluctance. Create scenes that evoke feelings of loss and longing. Ensure the tone is somber and reflective." Rhetorical Technique Application (RTA): Enhancing Language Expression Theoretical Basis of RTA: RTA is based on the theories of rhetoric and stylistics, proposing the following key steps: Implementation Steps of RTA: Determine the Task Objective: Clearly define the main purpose of the text. Choose Core Rhetorical Devices: Select 2-3 main rhetorical techniques. Design Rhetorical Examples: Create examples of how to use the selected techniques. Arrange Rhetorical Distribution: Plan how to distribute the rhetorical techniques throughout the text. Create Balance Strategies: Ensure the techniques are not overly forced or excessive. Generate Rhetorical Application Prompts: Create prompts guiding the AI to use rhetorical techniques. Application Example: Task: Guide the AI to generate a short story describing a city's nightlife. (1) Rhetorical Technique Selection: Main Techniques: Metaphor, personification, parallelism Auxiliary Techniques: Contrast, exaggeration (2) Contextual Appropriateness: Ensure the techniques fit the context of the city's nightlife. (3) Technique Integration: Combine the techniques to create a vivid and engaging description. (4) Effect Evaluation: Assess how effectively the techniques enhance the text. (5) Generate Rhetorical Application Prompts: Example Prompt: "Write a short story describing a city's nightlife. Use metaphors, personification, and parallelism to create a vivid and engaging description. Incorporate contrast and exaggeration to enhance the atmosphere." Integration of Stylistic Simulation, Emotional Integration, and Rhetorical Techniques: To effectively combine stylistic simulation, emotional integration, and rhetorical techniques, consider the following strategies: Language Style Optimization: Integrate emotional and rhetorical elements into the chosen style to enhance the overall impact. Contextual Consistency: Ensure that all elements align with the context and purpose of the text. Iterative Refinement: Continuously refine the text to achieve a harmonious blend of style, emotion, and rhetoric.

Table of ContentsWhat is DeepSeek?What Can DeepSeek Do?How to Use DeepSeek? What is DeepSeek? AI + Domestic + Free + Open Source + Powerful DeepSeek is a Chinese tech company specializing in General Artificial Intelligence (AGI), focusing on the development and application of large models. DeepSeek-R1 is its open-source reasoning model, excelling in handling complex tasks and available for free commercial use. DeepSeek from entry to mastery (Tsinghua University) PDF Downlod What Can DeepSeek Do? Text Generation Structured Generation: Tables, lists (e.g., schedules, recipes) Document Writing: Code comments, documentation Creative Writing: Articles, stories, poetry, marketing copy, social media content, scripts, etc. Summarization and Rewriting: Long text summaries (papers, reports), text simplification, multilingual translation and localization Natural Language Understanding and Analysis Knowledge Reasoning: Logical problem-solving (math, common sense reasoning), causal analysis (event correlation) Semantic Analysis: Sentiment analysis (reviews, feedback), intent recognition (customer service, user queries), entity extraction (names, locations, events) Text Classification: Topic labeling (e.g., news categorization), spam content detection Programming and Code-Related Tasks Code Generation and Completion: Code snippets (Python, JavaScript), auto-completion with comments Code Debugging: Error analysis and repair suggestions, performance optimization tips Technical Documentation: API documentation, codebase explanation and example generation Conventional Drawing (Not explicitly mentioned but implied through general capabilities) How to Use DeepSeek? Access:https://chat.deepseek.com From Beginner to Master: When everyone can use AI, how can you use it better and more effectively? Reasoning Models Example: DeepSeek-R1, GPT-3 excel in logical reasoning, mathematical reasoning, and real-time problem-solving. Reasoning models are models that enhance reasoning, logical analysis, and decision-making capabilities on top of traditional large language models. They often incorporate additional technologies such as reinforcement learning, neuro-symbolic reasoning, and meta-learning to strengthen their reasoning and problem-solving abilities. Non-reasoning models are suitable for most tasks. General models typically focus on language generation, context understanding, and natural language processing, without emphasizing deep reasoning capabilities. These models usually grasp language patterns through extensive text data training and can generate appropriate content, but they lack the complex reasoning and decision-making abilities of reasoning models. Dimension Comparison Dimension Reasoning Model General Model Strengths Mathematical derivation, logical analysis, code generation, complex problem decomposition Text generation, creative writing, multi-turn dialogue, open-ended questions Weaknesses Divergent tasks (e.g., poetry creation) Tasks requiring strict logical chains (e.g., mathematical proofs) Performance Essence Specializes in tasks with high logical density Excels in tasks with high diversity Strength Judgment Not universally stronger, but significantly better in their training target domains More flexible in general scenarios, but requires prompt engineering to compensate for capabilities Example Models GPT-3, GPT-4 (OpenAI), BERT (Google): Mainly used for language generation, language understanding, text classification, translation, etc. Fast Thinking vs. Slow Thinking Fast Reaction Models (e.g., ChatGPT-4): Quick response, low computational cost, based on probability prediction through extensive data training Slow Thinking Models (e.g., OpenAI-1): Slow response, high computational cost, based on chain-of-thought reasoning to solve problems step-by-step Decision-Making: Fast reaction models rely on pre-set algorithms and rules, while slow thinking models can make autonomous decisions based on real-time analysis Creativity: Fast reaction models are limited to pattern recognition and optimization, while slow thinking models can generate new ideas and solutions Human Interaction: Fast reaction models follow pre-set scripts and struggle with human emotions and intentions, while slow thinking models can interact more naturally and understand complex emotions and intentions Problem-Solving: Fast reaction models excel in structured and well-defined problems, while slow thinking models can handle multi-dimensional and unstructured problems, providing creative solutions Ethical Issues: Fast reaction models as controlled tools have minimal ethical concerns, while slow thinking models raise discussions on autonomy and control If you're passionate about the AI field and preparing for AWS or Microsoft certification exams, SPOTO have comprehensive and practical study materials ready for you. Whether you're preparing for AWS's Machine Learning certification (MLA-C01), AI Practitioner certification (AIF-C01), or Microsoft's AI-related exams (AI-900, AI-102), the certification materials I offer will help you study efficiently and increase your chances of passing. Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (click this) AWS AIF-C01 study materials (click this) AWS MLS-C01 study materials (click this) Microsoft AI-900 study materials (click this) Microsoft AI-102 study materials (click this) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! CoT Chain-of-Thought The emergence of CoT chain-of-thought divides large models into two categories: "probability prediction (fast reaction)" models and "chain-of-thought (slow thinking)" models. The former is suitable for quick feedback and immediate tasks, while the latter solves complex problems through reasoning. Understanding their differences helps in choosing the appropriate model for the task to achieve the best results. Prompt Strategy Differences Reasoning Models: Prompts should be concise, focusing directly on the task goal and requirements (as reasoning logic is internalized). Avoid step-by-step guidance, as it may limit the model's capabilities. General Models: Prompts need to explicitly guide reasoning steps (e.g., through CoT prompts), otherwise, the model may skip key logic. Rely on prompt engineering to compensate for capability shortcomings. Key Principles Model Selection: Choose based on task type, not model popularity (e.g., reasoning models for math tasks, general models for creative tasks). Prompt Design: Reasoning Models: Use concise instructions, focus on the goal, and trust the model's internalized reasoning capabilities. ("Just say what you want.") General Models: Use structured and compensatory guidance. ("Fill in what's missing.") Avoid Pitfalls: Do not use heuristic prompts (e.g., role-playing) with reasoning models, as it may interfere with their logical mainline. Do not over-trust general models (e.g., directly asking complex reasoning questions); instead, validate results step-by-step. From "Giving Instructions" to "Expressing Needs" Strategy Types Strategy Type Definition & Goal Applicable Scenarios Example (for Reasoning Models) Advantages & Risks   Instruction-Driven Directly provide clear steps or format requirements Simple tasks, quick execution "Write a quicksort function in Python with comments." ✅ Precise and efficient results ✕ Limits model's optimization space Demand-Oriented Describe problem background and goals, let the model plan the solution path Complex problems, model's autonomous reasoning "Optimize the user login process by analyzing current bottlenecks and proposing 3 solutions." ✅ Stimulates model's deep reasoning ✕ Need to clearly define demand boundaries Hybrid Mode Combine problem description with key constraints Balance flexibility and controllability "Design a 3-day travel plan for Hangzhou, including West Lake and Lingyin Temple, with a budget of 2000 yuan." ✅ Balances goals and details ✕ Avoid over-constraining Heuristic Questioning Guide the model to think actively through questions (e.g., "why," "how") Exploratory problems, model's explanatory logic "Why choose gradient descent for this optimization problem? Compare with other algorithms." ✅ Triggers model's self-explanation ability ✕ May deviate from core goals Task Demand and Prompt Strategy Task Type Applicable Model Prompt Focus Example (Effective Prompt) Prompts to Avoid Mathematical Proof Reasoning Model Direct questioning, no step-by-step guidance "Prove the Pythagorean theorem" Redundant decomposition (e.g., "First draw a diagram, then list formulas") Creative Writing Reasoning Model Encourage divergence, set roles/styles "Write an adventure story in Hemingway's style" Over-constraining logic (e.g., "List steps in chronological order") Code Generation Reasoning Model Concise needs, trust model logic "Implement quicksort in Python" Step-by-step guidance (e.g., "First write the recursive function") Multi-turn Dialogue General Model Natural interaction, no structured instructions "What do you think about the future of artificial intelligence?" Forced logical chains (e.g., "Answer in three points") Logical Analysis Reasoning Model Directly pose complex problems "Analyze the utilitarianism and deontological conflict in the trolley problem" Adding subjective guidance (e.g., "Which do you think is better?") General Model General Model Break down problems, ask step-by-step "First explain the trolley problem, then compare the two ethical views" One-time questioning of complex logic How to Express Needs to AI Demand Type Characteristics Demand Expression Formula Reasoning Model Adaptation Strategy General Model Adaptation Strategy Decision-Making Need to weigh options, assess risks, choose the best solution Goal + Options + Evaluation Criteria Request logical deduction and quantitative analysis Direct suggestions, rely on model's experience Analytical Need to deeply understand data/information, discover patterns or causal relationships Problem + Data/Information + Analysis Method Trigger causal chain deduction and hypothesis validation Surface summarization or classification Creative Need to generate novel content (text/design/solution) Theme + Style/Constraints + Innovation Direction Combine logical framework to generate structured creativity Free association, rely on example guidance Verification Need to check logical consistency, data reliability, or solution feasibility Conclusion/Solution + Verification Method + Risk Points Design verification path independently and identify contradictions Simple confirmation, lack of deep deduction Execution Need to complete specific operations (code/calculation/process) Task + Step Constraints + Output Format Optimize steps autonomously, balance efficiency and correctness Strictly follow instructions, no autonomous optimization Prompt Examples Decision-Making Demand: "Two options are available to reduce logistics costs: ① Build a regional warehouse (high initial investment, low long-term costs) ② Partner with a third party (pay-as-you-go, high flexibility) Please use the ROI calculation model to compare the total costs over 5 years and recommend the optimal solution." Verification Demand: "Here is a conclusion from a paper: 'Neural network model A is superior to traditional method B.' Please verify: ① Whether the experimental data supports this conclusion; ② Check if there is any bias in the control group setup; ③ Recalculate the p-value and determine significance." Analytical Demand: "Analyze the sales data of new energy vehicles over the past three years (attached CSV), and explain: ① The correlation between growth trends and policy; ② Predict the market share in 2025 using the ARIMA model and explain the basis for parameter selection." Execution Demand: "Convert the following C code to Python, with the following requirements: ① Maintain the same time complexity; ② Use numpy to optimize array operations; ③ Output the complete code with time test cases." Creative Demand: "Design a smart home product to address the safety issues of elderly people living alone, combining sensor networks and AI early warning. Provide three different technical route prototype sketches with explanations." Verification Demand: "Below is a conclusion from a paper: 'Neural network model A is superior to traditional method B.' Please verify: ① Whether the experimental data supports this conclusion; ② Check if there is any bias in the control group setup; ③ Recalculate the p-value and determine significance." Do We Still Need to Learn Prompts? Prompts are the instructions or information that users input into an AI system to guide it to generate specific outputs or perform specific tasks. Simply put, prompts are the language we use to "converse" with AI. They can be a simple question, a detailed instruction, or a complex task description. A prompt consists of three basic elements: Instruction (Instruction): The core of the prompt, explicitly telling the AI what task to perform. Context (Context): Providing background information to help the AI better understand and execute the task. Expectation (Expectation): Clearly or implicitly expressing the requirements and expectations for the AI's output. Types of Prompts Instructional Prompts: Directly tell the AI what task to perform. Question-Answer Prompts: Pose questions to the AI, expecting corresponding answers. Role-Playing Prompts: Require the AI to assume a specific role and simulate a particular scenario. Creative Prompts: Guide the AI to perform creative writing or content generation. Analytical Prompts: Require the AI to analyze and reason about given information. Multimodal Prompts: Combine text, images, and other forms of input. The Essence of Prompts Feature Description Example Communication Bridge Connects human intent with AI understanding "Translate the following into French: Hello, world" Context Provider Provides necessary background information for the AI "Assuming you are a 19th-century historian, comment on Napoleon's rise" Task Definer Clearly specifies the task the AI needs to complete "Write an introduction for an article on climate change, 200 words" Output Shaper Influences the form and content of the AI's output "Explain quantum mechanics in simple terms, as if speaking to a 10-year-old" AI Capability Guide Guides the AI to use specific abilities or skills "Use your creative writing skills to create a short story about time travel" Article from:Team: Yu Menglong, Postdoctoral Fellow Tsinghua University School of Journalism and Communication New Media Research Center, Metaverse Culture Lab  

Table of ContentsWhy Choose Fortinet Certification?Fortinet Certification Path: The New StructureHow to Prepare for Fortinet CertificationsCareer Benefits of Fortinet CertificationFinal Thoughts In the ever-evolving field of cybersecurity, Fortinet has become a leader in providing robust security solutions. Its certification program is designed to equip IT professionals with the necessary skills to manage and secure Fortinet networks effectively. If you're new to Fortinet certifications or considering one, this guide will walk you through everything you need to know. Why Choose Fortinet Certification? Fortinet certifications are highly valued in the cybersecurity industry, providing professionals with credibility, hands-on expertise, and career growth opportunities. These certifications validate your ability to configure, manage, and troubleshoot Fortinet security products, making you a valuable asset to employers looking for skilled network security professionals. Fortinet Certification Path: The New Structure As of October 2023, Fortinet revamped its certification structure to offer a more flexible and specialized learning path. The new framework consists of five proficiency levels, allowing candidates to advance from fundamental knowledge to expert-level mastery. 1. Fortinet Certified Fundamentals (FCF) This is the entry-level certification designed for beginners in cybersecurity. It introduces basic networking concepts, security principles, and an overview of Fortinet technologies. Ideal for: Individuals new to IT security Professionals looking to understand Fortinet's security framework   2. Fortinet Certified Associate (FCA) At this level, candidates gain a more structured understanding of Fortinet products and security solutions. It covers fundamental networking and firewall concepts with a focus on FortiGate. Ideal for: IT professionals with basic knowledge of network Those looking to specialize in Fortinet security solutions   3. Fortinet Certified Professional (FCP) The FCP level validates an individual's ability to configure and deploy Fortinet solutions effectively. It covers various Fortinet technologies such as FortiGate, FortiAnalyzer, FortiManager, and more. Ideal for: Network and security professionals managing Fortinet devices IT administrators responsible for security implementations   4. Fortinet Certified Security Specialist (FCSS) This advanced certification is for specialists who want to demonstrate deep expertise in specific areas of Fortinet security. Candidates can choose specialized tracks such as: FortiWeb (Web Application Security) FortiNAC (Network Access Control) FortiSIEM (Security Information and Event Management)   Ideal for: Cybersecurity professionals seeking a specialization IT professionals managing complex security infrastructures   5. Fortinet Certified Expert (FCX) The FCX is the highest certification level, proving mastery of Fortinet's security solutions. It requires extensive experience and advanced knowledge of Fortinet's security fabric. This certification is highly regarded in the industry and is meant for those looking to lead security teams or design enterprise-level security architectures. Ideal for: Senior network security architects IT professionals aiming for top-tier cybersecurity roles   How to Prepare for Fortinet Certifications Getting certified requires dedication, hands-on practice, and the right study approach. Here are some beneficial ways: 1. Choose the Right Study Resources Fortinet offers free and paid training resources, including: Fortinet NSE Training Institute (Official learning platform) Fortinet Network Security Academy (FNSA) (For students and institutions) Online courses from platforms like SPOTO   2. Get Hands-On Experience Practical experience with Fortinet security appliances is crucial. You can: Set up FortiGate virtual labs Use Fortinet's online demo environments Practice configurations with real-world scenarios   3. Take Practice Exams Before attempting the actual certification exam, test your knowledge with mock exams. This will: Help you grasp the format of the exam Identify weak areas for improvement Improve time management during the test   4. Join Fortinet Communities and Forums Engaging with professionals and certified experts can provide valuable insights. Popular communities include: Reddit's r/networking and r/fortinet groups LinkedIn cybersecurity groups   Career Benefits of Fortinet Certification 1. Increased Job Opportunities Fortinet certifications open doors to high-demand cybersecurity roles, including: Network Security Engineer Security Operations Center (SOC) Analyst Cybersecurity Consultant   2. Competitive Salary Certified Fortinet professionals often earn higher salaries compared to non-certified peers. Roles requiring Fortinet expertise can pay upwards of $100,000+ per year, depending on experience and location. 3. Industry Recognition Fortinet is a leading cybersecurity provider, and having its certifications boosts your credibility in network security, enterprise security, and cloud security solutions. Final Thoughts Fortinet certifications are a great way to validate your cybersecurity expertise and advance your career in network security. Whether you're a beginner looking for foundational knowledge or an experienced professional aiming for expert-level mastery, the new Fortinet certification path offers a structured approach to skill development and career growth. If you're ready to start your Fortinet certification journey, explore official training materials, get hands-on practice, and engage with the Fortinet community to maximize your chances of success.