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Training goals

code: AWS-DD-IPE | version: v2025

In this course, you will learn how to create and optimize prompts for a variety of generative AI models. First, this course covers the basics of foundation models, including a subset of foundation models (FMs), called large language models (LLMs). Then, the course covers the fundamental concepts of prompt engineering, such as the different elements of a prompt and some general best practices for using prompts effectively. Finally, the course provides information about basic prompt techniques, including zero-shot, few-shot, and chain-of-thought (CoT) prompting.

  • Level: Fundamental
  • Duration: 60 minutes

 

Learning objectives

During this event, you will learn:

  • Identify the fundamental concepts of FMs and LLMs
  • Define prompt engineering and identify the best practices for designing effective prompts
  • Identify the basic types of prompt techniques, including zero-shot, few-shot, and CoT techniques

 

Intended audience

This event is intended for:

  • Prompt engineers
  • Data scientists
  • Developers

 

Recommended follow-up training and resources

We recommend that attendees of this event continue learning with this course:

 

Conspect Show list

  • Module 1: Foundation models and large language models
    In this module, you will develop a fundamental understanding of FMs, including an understanding of a subset of FMs called LLMs. First, you will be introduced to the basic concepts of a foundation model, such as self-supervised learning and fine-tuning. Next, you will learn about two types of FMs: text-to-text models and text-to-image models. Finally, you will learn about LLMs' functionality and use cases, the subset of foundation models that most often utilize prompt engineering.
    • How does a foundation model function?
    • Training FMs
    • Types of FMs
    • Large language models
    • Transformer architecture
    • Neural networks
    • LLM use cases
  • Module 2: Key concepts of prompt engineering
    In this module, you are introduced more fully 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. Then, you learn about the different elements of a prompt. Finally, the module provides a list of general best practices for designing effective prompts, and you can participate in voting for which prompts showcase those best practices.
    • Fine-tuning and prompt engineering
    • Elements of a prompt
    • Best practices for designing effective prompts
    • Practice with prompts
  • Module 3: Basic prompt techniques
    In this module, you will learn about basic prompt engineering techniques that can help you effectively use generative AI applications for your unique business objectives. First, the module defines zero-shot and few-shot prompting techniques. Then, it defines CoT prompting, the building block for several advanced prompting techniques. This module provides tips and examples of each type of prompt technique.
    • Zero-shot prompting
    • Few-shot prompting
    • Chain-of-thought prompting
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Additional information

Difficulty level
Duration 1 day
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