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Foundations of Prompt Engineering - AWS Prompt Engineering Mastery

Precision Prompts, Powerful AI: From Basics to Bias-Resistant AI​​
  • ​​Master core techniques​​
  • Apply zero-shot prompts for simple tasks​​
  • Chain-of-thought (CoT) prompting for complex reasoning
  • ​​Optimize model outputs
  • Mitigate risks​​
  • ​​Leverage advanced frameworks
  • Align with industry
  • Build a base for specialized roles in AI development or cloud engineering
  • Prepare for more advanced AWS certifications in AI

What you will learn

​​"Foundations of Prompt Engineering"​​ is a 4-hour intermediate-level AWS course designed for prompt engineers, data scientists, and developers aiming to master ​​generative AI interaction strategies​​. The curriculum bridges foundational concepts like zero-shot/few-shot learning with advanced techniques such as Retrieval Augmented Generation (RAG) and bias mitigation. Learners explore model-specific optimizations for Amazon Titan, Claude, and Jurassic-2, while addressing adversarial risks like prompt injection. Hands-on examples and best practices equip professionals to design precise prompts for business automation, ethical AI deployment, and enhanced model performance.

AWS's Foundations of Prompt Engineering course Outline

Learn the Foundations of Prompt Engineering

Want to transform vague AI outputs into precision tools? ​​AWS's Foundations of Prompt Engineering​​ teaches you to craft prompts that unlock the full potential of Claude, Titan, and other models. In 4 hours, learn bias mitigation, RAG workflows, and defense against prompt hijacking—no prior expertise needed. This course includes real-world examples and AWS-specific optimizations, which are perfect for developers and data scientists. Ready to engineer smarter AI interactions? Click the link to start mastering prompt design today!

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Course Structure Includes:

Lesson 1: Basics of Large Language Models

In this lesson, you will develop a fundamental understanding of foundation models (FMs), including an understanding of a subset of FMs called large language models (LLMs). First, you will be introduced to the basic concepts of a foundation model such as self-supervised learning and finetuning. Next, you will learn about two types of FMs: text-to-text models and text-to-image models. Finally, you will learn about the functionality and use cases of LLMs, the subset of foundation models that most often utilize prompt engineering.

 

Lesson 2: Fundamentals of Prompt Engineering

In this lesson, you are introduced to prompt engineering, the set of practices that focus on developing, designing, and optimizing prompts to enhance the output of FMs for your specific business needs. This lesson first defines prompt engineering and describes the key concepts and terminology of prompt engineering. Then, the lesson uses an example prompt to show the different elements of a prompt. Finally, the lesson provides a list of general best practices for designing effective prompts.

 

Lesson 3: Basic Prompt Techniques

In this lesson, you will learn about basic prompt engineering techniques that can help you use generative AI applications effectively for your unique business objectives. First, the lesson defines zero-shot and few-shot prompting techniques. Then, the lesson defines chain-of-thought (CoT) prompting, the building block for several advanced prompting techniques. This lesson provides tips and examples of each type of prompt technique.

 

Lesson 4: Advanced Prompt Techniques

In this lesson, you will be introduced to several advanced techniques including: Self Consistency, Tree of Thoughts, Retrieval augmented generation (RAG), Automatic Reasoning and Tool-use (ART), ReAct, and LangChain. Examples are provided to show each technique in practice.

 

Lesson 5: Model-specific Prompt Techniques

In this lesson, you will learn how to engineer prompts for a few of the most popular FMs including Amazon Titan, Anthropic Claude, and AI21 Labs Jurassic-2. You will learn about the different parameters you can configure to get customized results from the models. Next you will learn about prompt engineering best practices for each of the models.

 

Lesson 6: Addressing Prompt Misuses

In this lesson, you will be introduced to adversarial prompts, or prompts that are meant to purposefully mislead models. You will be learning about prompt injection and prompt leaking, two types of adversarial prompts. You will be provided with examples of each.

 

Lesson 7: Mitigating Bias

In this lesson, you will learn how bias is introduced into models during the training phase and how that bias can be reproduced in the responses generated by an FM. You will learn how biased results can be mitigated by updating the prompt, enhancing the dataset, and using training techniques.

Training Options

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Description

Master prompt engineering in 4 hours: Learn zero-shot to RAG techniques, mitigate bias, and secure AI workflows with AWS models like Titan and Claude.

Pre-requisites

AWS Technical Essentials

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