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. 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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.

The CCIE Data Center (CCIE DC) exam is one of the most challenging and prestigious certifications in the IT world. It's designed for professionals who want to validate their expertise in data center technologies such as networking, storage, automation, and cloud computing. Earning the CCIE DC certification opens doors to high-level roles in data center design, implementation, and management. However, due to its complexity, passing the exam requires a well-thought-out strategy, extensive preparation, and consistent practice. This guide breaks down 10 essential steps to help you pass the CCIE Data Center exam with confidence. Whether you're a seasoned IT professional or a newcomer to data center technologies, these steps will help you streamline your study plan and maximize your chances of success. 1. Understand the Exam Structure and Requirements Before diving into the preparation, familiarize yourself with the exam structure. The CCIE DC exam consists of two main parts: Written Exam (350-601 DCCOR): This exam tests your theoretical knowledge of various data center technologies. Lab Exam: A hands-on practical exam that requires you to configure and troubleshoot a series of network and data center solutions within a limited time frame.   Understanding the topics covered and how they are weighted will help you allocate your study time more effectively. Key areas include: Network Technologies (routing, switching, L2/L3 protocols) Data Center Infrastructure (storage, server, network integration) Automation and Orchestration (using automation tools like Python, and Ansible) Security and Network Services (firewalls, VPNs, QoS, etc.)   2. Master the Core Data Center Concepts To succeed in the CCIE DC exam, you need to have a strong understanding of the core data center concepts. These concepts include the fundamentals of virtualization, storage networking, compute technologies, and networking protocols. Key areas to focus on: Unified Computing System (UCS) and Compute Architecture Cisco Nexus switches and data center networking Storage Area Networks (SAN), FCoE, and iSCSI Virtualization technologies like VMware, Hyper-V, and Cisco ACI.   Having a solid grasp of these concepts will make it easier to handle more complex exam scenarios later. 3. Use Official Cisco Study Materials Cisco offers a range of official study materials to help you prepare for the CCIE DC exam. This includes the Cisco CCIE Data Center study guide and official training courses. These resources are structured to help you learn each topic systematically and ensure you don't miss out on critical areas. Key resources include: Cisco Press CCIE Data Center Study Guide Cisco Learning Network Cisco's online training courses (available via Cisco Digital Learning or Cisco Live!)   4. Build a Home Lab or Use Virtual Labs The lab exam is a critical part of the CCIE DC certification, so practicing hands-on is essential. You'll need to set up your own home lab or use virtual labs to get comfortable with Cisco devices and technologies. For your home lab, consider: Using Cisco Packet Tracer or GNS3 for simulation. Purchasing used equipment from reputable sources for a low-cost physical setup. Taking advantage of SPOTO for virtual labs.   5. Practice, Practice, Practice One of the most important steps in preparing for the CCIE DC exam is to practice as much as possible. Regular hands-on practice will help you build muscle memory for configuration tasks and troubleshooting scenarios. Focus on: Configuring VLANs, trunks, and routing protocols on Cisco devices. Setting up and troubleshooting storage networking (e.g., FCoE, iSCSI). Automating data center operations with tools like Python and Ansible. Running through configuration scenarios from past exams or study materials.   6. Review and Master the Cisco Documentation Cisco's official documentation is an essential resource for the exam. In many cases, you will be allowed to reference Cisco docs during the lab exam. Familiarizing yourself with these documents will save you valuable time when troubleshooting issues during the exam. Key documentation to focus on: Cisco Data Center Documentation (networking, storage, UCS, etc.) Cisco ACI and Cisco NX-OS documentation Automation and scripting documentation for Python and Ansible   Learn to navigate and find the information you need quickly. 7. Join a Study Group or Online Forum Joining a study group or participating in an online forum can provide support and motivation during your preparation. These communities are great for discussing difficult topics, sharing resources, and learning from others' experiences. Consider: Joining the CCIE Data Center sub-forum on the Cisco Learning Network. Engaging with CCIE DC communities on Reddit or LinkedIn. Participating in study groups and boot camps offered by training providers like INE, SPOTO, or NetworkLessons.   8. Time Management and Exam Strategy Time management is crucial during the lab exam. You will be tested under time pressure, so it's important to practice managing your time effectively. Make sure you: Prioritize tasks by tackling easier configurations first. Allocate a set amount of time to each task (configuration and troubleshooting) and stick to it. Leave at least 10–15 minutes at the end for reviewing and troubleshooting.   During the written exam, practice taking timed mock exams to ensure you can answer questions within the allotted time. 9. Understand the Troubleshooting Process A significant portion of the CCIE DC lab exam involves troubleshooting. Knowing the troubleshooting methodology is critical to quickly diagnosing and resolving issues. Key steps to follow: Verify connectivity: Ensure all devices are reachable. Check configuration: Look for misconfigurations that could affect network behavior. Isolate the problem: Narrow down the issue to a specific area of the network or technology. Fix and test: Apply changes and verify the fix.   Having a structured troubleshooting approach will help you efficiently tackle issues during the exam. 10. Stay Calm and Confident The CCIE DC exam can be daunting, but confidence is key. Stay calm, stick to your study plan, and take regular breaks to avoid burnout. Make sure you: Get plenty of rest before the exam. Stay hydrated and focused during the test. Remember that mistakes happen—stay positive and move forward.   Conclusion Passing the CCIE Data Center exam requires hard work, dedication, and a solid study strategy. By following these 10 essential steps, you'll build a strong foundation of knowledge and skills that will help you succeed. The journey to becoming a CCIE may be challenging, but with the right preparation, you can approach the exam with confidence and take your career to new heights.

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.

In the rapidly evolving landscape of IT, network automation and programmability are no longer just trends; they're becoming the norm. Cisco's CCIE DevNet certification is at the forefront of this transformation, bridging the gap between networking and software development. As more organizations adopt automation to streamline their operations, demand for professionals with expertise in both networking and software is growing exponentially. The CCIE DevNet track is designed for professionals who want to specialize in network automation, APIs, Python scripting, and network programmability. This certification represents the future of networking, where traditional network engineers merge their skills with modern software development techniques. What is CCIE DevNet? The CCIE DevNet certification focuses on integrating software development with networking principles. It teaches candidates how to automate network configurations, work with APIs, and apply DevOps practices to networking tasks. By mastering this combination, DevNet professionals can improve network performance, increase efficiency, and reduce human error in configuration management. In short, CCIE DevNet is ideal for network engineers looking to transform their traditional roles into more software-centric, automation-driven positions. Key Areas of CCIE DevNet This track includes several core areas that define network automation and programmability: Network Automation One of the primary goals of DevNet is to automate repetitive network tasks. This involves using tools like Ansible, Python scripts, and Cisco DNA Center to automate network provisioning, configuration, and monitoring. Automation reduces the potential for human error and improves operational efficiency. Network APIs and SDKs A significant portion of DevNet focuses on understanding and using APIs to interact with Cisco devices. By learning how to call APIs, network professionals can automate network management and extend functionality. Cisco offers a variety of software development kits (SDKs) to help engineers write programs that interface with their networking hardware and software. Programmability with Python Python is the go-to programming language for network automation. DevNet professionals need to be proficient in Python scripting to interact with APIs, automate workflows, and perform tasks like collecting data from network devices and running tests. Python's ease of use and readability make it a preferred language for automating networking tasks. DevOps Practices in Networking While DevOps originated in the software development world, its principles are now widely applied in network engineering. DevNet encourages network engineers to adopt a collaborative, continuous integration, and continuous delivery (CI/CD) mindset when working with network configurations. This makes it easier to deploy changes to the network in a controlled, predictable manner. Security and Monitoring Security remains a key consideration in network automation. DevNet ensures that candidates understand the importance of securing automation scripts, maintaining network security, and continuously monitoring the network's health through automated processes.   Why Choose CCIE DevNet? The demand for network automation is on the rise as businesses strive to manage increasingly complex network infrastructures. Here are a few reasons why you should consider CCIE DevNet: High Demand for Skills As more businesses adopt SDN (Software-Defined Networking), cloud solutions, and IoT (Internet of Things) technologies, the need for network engineers who can automate and program these environments is growing. CCIE DevNet provides you with the expertise needed to excel in this field. Lucrative Career Opportunities Professionals with DevNet skills can command competitive salaries due to the specialized nature of the role. DevNet engineers are in demand across industries, from telecommunications to cloud services. Future-Proofing Your Career Networking is becoming less about configuring routers and more about programming the network. By earning a CCIE DevNet, you position yourself at the intersection of networking and software development, ensuring your skills remain relevant for years to come. Hands-On Learning The CCIE DevNet exam is designed to test real-world skills, requiring candidates to solve complex, practical problems. You'll gain hands-on experience with tools like Cisco NSO, Cisco DNA Center, and Python, which are integral to modern networking environments.   CCIE DevNet Certification: What to Expect The CCIE DevNet exam consists of two parts: Written Exam The CCIE DevNet Written Exam tests your understanding of networking concepts, automation, APIs, and security. It serves as the foundation for the more advanced lab exam. Lab Exam The Lab Exam is a hands-on assessment where candidates demonstrate their ability to automate network tasks, configure devices, and work with APIs in real-world scenarios. The lab exam is challenging and requires extensive preparation.   Skills Gained from CCIE DevNet Upon certification, you'll have the skills to: Automate complex network configurations and operations using Python and APIs. Design, develop, and implement network solutions that leverage software tools and automation. Monitor and troubleshoot networks with automated scripts and diagnostic tools. Integrate network automation into cloud and hybrid IT environments.   Conclusion CCIE DevNet is more than just a certification; it's an opportunity to redefine the way you approach networking. By merging software development and network engineering, you'll be part of a growing movement that is shaping the future of IT. If you're passionate about automating networks, learning programming, and adopting DevOps practices, this is the certification that will propel your career into the future.

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.

Amazon Web Services (AWS) has become a dominant force in the cloud computing landscape, offering a comprehensive suite of services that range from computing power to storage, databases, machine learning, and more. For beginners, AWS might seem overwhelming due to its sheer volume of services and features. However, with a solid foundation in a few key areas, you'll be better prepared to take advantage of what AWS has to offer. In this article, we'll break down the essential knowledge you need before you dive into the world of AWS. 1. Basic Networking Knowledge At its core, AWS is all about hosting and managing resources over the internet. To effectively navigate and configure AWS, you should have a basic understanding of networking concepts. AWS provides a virtual network environment where you'll configure and manage networking components such as VPCs (Virtual Private Clouds), subnets, and security groups. Key concepts to know: TCP/IP and DNS: These are fundamental to how data is transmitted over the internet and how AWS resolves domain names to IP addresses. IP Addressing: You need to understand how private and public IP addresses work, as well as subnetting to efficiently segment your network in AWS. Virtual Private Cloud (VPC): AWS VPC lets you define and control a virtual network within the AWS cloud, including choosing IP address ranges, subnets, and route tables. Security Groups and NACLs (Network Access Control Lists): These act as firewalls to control inbound and outbound traffic at the instance level (security groups) or subnet level (NACLs).   2. Understanding Cloud Computing Concepts Before you start exploring AWS, it's essential to have a grasp of cloud computing fundamentals. Cloud services differ from traditional on-premises infrastructure in many ways, and understanding these differences will help you make the most of AWS. Key concepts to understand: Cloud Models: AWS operates primarily under the IaaS (Infrastructure as a Service) and PaaS (Platform as a Service) models. You should understand the difference between these models and SaaS (Software as a Service). On-Demand vs. Reserved: AWS offers both on-demand and reserved instances. On-demand lets you pay for computing power as you need it, while reserved instances offer discounted rates for long-term commitments. Scalability and Elasticity: One of the key features of cloud computing is the ability to scale your resources up or down depending on your needs. AWS makes this process automated and flexible, allowing you to handle changing workloads.   3. Basic Command Line Knowledge (Especially Linux) While AWS provides a management console for most tasks, much of the real power comes from the AWS CLI (Command Line Interface) and API calls. Therefore, having familiarity with the command line (especially on Linux systems) will be beneficial. Many AWS services can be efficiently managed through the CLI, and a lot of tasks—such as launching EC2 instances or managing S3 storage—can be streamlined via scripts. Key concepts to learn: Linux/Unix commands: Many AWS services run on Linux-based systems, so understanding file management, permissions, and commands (like ls, cp, mv, chmod, chmod) will be helpful. Bash scripting: Learning how to write simple bash scripts will help you automate repetitive tasks in AWS. AWS CLI: The AWS CLI provides a powerful command-line interface to interact with AWS resources. You can manage instances, databases, storage, and more from the terminal using simple commands.   4. Understanding AWS Core Services AWS offers a wide array of services, but for beginners, it's important to focus on the core services that you'll use most frequently. These are the building blocks of AWS and understanding them will give you a strong foundation. Essential services to know: EC2 (Elastic Compute Cloud): EC2 allows you to run virtual machines in the cloud. You'll need to understand how to launch, manage, and terminate EC2 instances, as well as how to configure them for specific use cases. S3 (Simple Storage Service): S3 provides scalable object storage. You should learn how to upload, organize, and manage your data within buckets. RDS (Relational Database Service): RDS simplifies the process of setting up and managing relational databases in the cloud. Understanding RDS will be helpful if you plan to use AWS for database management. IAM (Identity and Access Management): IAM is crucial for managing security and access to your AWS resources. You should understand how to create users, groups, and roles, and how to manage permissions to secure your cloud environment.   5. Familiarity with AWS Pricing Models Cloud computing comes with various pricing options, and AWS is no different. It's crucial to understand how AWS charges for its services to avoid unexpected costs. Key pricing concepts to understand: Pay-as-you-go: Most AWS services operate on a pay-as-you-go pricing model, meaning you only pay for the resources you use. Free Tier: AWS offers a free tier that allows you to experiment with many services at no cost for the first 12 months. While the free tier is limited, it's a great way to start learning without incurring charges. Cost Explorer: AWS provides tools like Cost Explorer and Budgets to help you track and manage your usage and spending.   6. Security Best Practices Security is a critical concern in the cloud, and AWS offers a variety of tools to help you secure your environment. Learning AWS's security best practices will help you protect your data and applications. Key concepts to explore: Encryption: AWS provides encryption tools to protect data both at rest and in transit. Learn how to implement encryption on S3, EBS volumes, and in database storage. Access Control: Using IAM, you can manage access to your AWS resources by creating users and roles with specific permissions. Security Groups and NACLs: As mentioned earlier, these are vital for controlling traffic and ensuring that your resources are only accessible to authorized users.   Conclusion Before you try AWS, it's important to lay a solid foundation in networking, cloud computing basics, command-line skills, AWS's core services, pricing models, and security best practices. AWS offers powerful, scalable, and flexible cloud solutions, but understanding these fundamentals will ensure that you can use them efficiently and avoid common pitfalls. Once you're comfortable with these concepts, you'll be ready to dive into the more advanced aspects of AWS and harness the full power of cloud computing.  

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 ContentsCCIE Tracks OverviewChoosing the Right CCIE TrackBenefits of Becoming a CCIEConclusion In the fast-evolving world of networking, the Cisco Certified Internetwork Expert (CCIE) certification stands as the pinnacle of technical excellence. Recognized globally, CCIE validates an expert-level understanding of networking principles and solutions. For professionals seeking to deepen their expertise and boost career prospects, selecting the right CCIE track is crucial. Each track caters to specific skills, industries, and technologies, making it essential to align your passion with your professional aspirations. This guide breaks down each CCIE track, helping you determine which specialization suits your career goals. Whether you're passionate about security, automation, wireless networking, or service provider networks, there's a track designed just for you. CCIE Tracks Overview Cisco currently offers seven CCIE tracks, each tailored to different networking domains. Let's dive into each specialization and what it entails. 1. CCIE Enterprise Infrastructure This track is the most popular, covering advanced routing, switching, SD-WAN, network automation, and security. It's ideal for engineers working in enterprise environments, ensuring they can design and troubleshoot complex network infrastructures. Who should choose this track? Network engineers managing enterprise-grade networks Professionals interested in network automation and programmability Those looking to master SD-WAN and software-defined networking (SDN)   2. CCIE Enterprise Wireless Wireless networking is at the heart of modern businesses, and this track focuses on wireless security, mobility services, and RF design. As organizations expand their wireless capabilities, experts in this field are in high demand. Who should choose this track? Wireless engineers designing and implementing Wi-Fi networks Professionals working with Cisco DNA Center and wireless security Those passionate about wireless troubleshooting and RF optimization   3. CCIE Security Cybersecurity threats are increasing, making CCIE Security one of the most sought-after specializations. This track covers firewalls, VPNs, network security policies, and advanced threat defense. Who should choose this track? Security professionals handling network and cloud security Engineers working with Cisco Secure Firewall, ISE, and VPNs Those passionate about penetration testing, threat mitigation, and compliance   4. CCIE Data Center This track focuses on modern data center infrastructure, including automation, storage networking, virtualization, and cloud computing. It prepares professionals to design and manage large-scale data centers. Who should choose this track? Cloud architects and data center engineers Those managing Cisco Nexus switches, UCS, and ACI Professionals looking to specialize in hybrid cloud solutions   5. CCIE Service Provider Telecommunications companies and ISPs require specialists to handle their MPLS networks, segment routing, QoS, and network automation. This track ensures seamless connectivity for large-scale service provider environments. Who should choose this track? Network engineers working for service providers and telecom companies Those managing MPLS networks, BGP, and segment routing Professionals interested in network programmability and automation   6. CCIE Collaboration Businesses rely on VoIP, video conferencing, and unified communications (UC) to stay connected. The CCIE Collaboration track focuses on designing and troubleshooting advanced collaboration solutions. Who should choose this track? Voice and UC engineers managing Cisco Unified Communications Manager (CUCM) Professionals working with Webex, Cisco Jabber, and IP telephony Those passionate about VoIP security, video solutions, and call routing   7. CCIE DevNet The newest addition to the CCIE family, CCIE DevNet, blends software development and networking. This track focuses on automation, APIs, Python scripting, and network programmability. Who should choose this track? Network engineers interested in automation and infrastructure as code (IaC) Developers looking to integrate networking with DevOps methodologies Professionals working with Cisco DNA Center, Python, and REST APIs   Choosing the Right CCIE Track With so many options, selecting the best CCIE track depends on your interests, career goals, and industry demand. Here are a few factors to consider: Passion and Strengths – If you enjoy securing networks, CCIE Security is a great fit. If you prefer automation and scripting, CCIE DevNet might be your calling. Industry Demand – Look at the job market. Enterprise Infrastructure and Security are in high demand, but Data Center and DevNet are rapidly growing fields. Long-Term Career Goals – Do you want to work in cloud computing, telecommunications, cybersecurity, or automation? Choose a track that aligns with your aspirations.   Benefits of Becoming a CCIE Regardless of the track you choose, earning a CCIE certification offers numerous benefits: Higher Salary Potential – CCIE-certified professionals earn some of the highest salaries in the networking industry. Industry Recognition – Being CCIE-certified sets you apart as an expert in your field. Career Growth – Many CCIE holders advance to roles like network architect, security consultant, and cloud solutions engineer.   Conclusion The CCIE certification is more than just a credential—it's a gateway to new opportunities, professional growth, and technological mastery. By choosing a track that aligns with your passion and career goals, you can elevate your skills and make a significant impact in the IT industry. Whether it's security, automation, enterprise networking, or cloud solutions, CCIE is where passion meets the profession.