Learning Objectives
The Objectives of this course are as follows:
- Describe generative AI and how it aligns to machine learning
- Define the importance of generative AI and explain its potential risks and benefits
- Identify business value from generative AI use cases
- Discuss the technical foundations and key terminology for generative AI
- Explain the steps for planning a generative AI project
- Identify some of the risks and mitigations when using generative AI
- Understand how Amazon Bedrock works
- Familiarize yourself with basic concepts of Amazon Bedrock
- Recognize the benefits of Amazon Bedrock
- List typical use cases for Amazon Bedrock
- Describe the typical architecture associated with an Amazon Bedrock solution
- Understand the cost structure of Amazon Bedrock
- Implement a demonstration of Amazon Bedrock in the AWS Management Console
- Define prompt engineering and apply general best practices when interacting with FMs
- Identify the basic types of prompt techniques, including zero-shot and few-shot learning
Target Audience
The course is intended for:
Prerequisite Experience
- AWS Technical Essentials
- Intermediate-level proficiency in Python
Course Outline
Introduction to Generative AI – Art of the Possible
- Overview of ML
- Basics of generative AI
- Generative AI use cases
- Generative AI in practice
- Risks and benefits
- Steps in planning a generative AI project
- Risks and mitigation
Getting Started with Amazon Bedrock
- Introduction to Amazon Bedrock
- Amazon Bedrock architecture and use cases
- How to use Amazon Bedrock
- Lab 1: Setting Up Bedrock Access
Foundations of Prompt Engineering
- Fundamentals of prompt engineering
- Basic prompt techniques
- Advanced prompt techniques
- Model-specific prompt techniques
- Addressing prompt misuses
- Mitigating bias
- Lab 2: Fine-Tuning a Basic Text Prompt
- Integrating AWS and LangChain
- Using models with LangChain
- Constructing prompts
- Structuring documents with indexes
- Storing and retrieving data with memory
- Using chains to sequence components
- Managing external resources with LangChain agents
- Introduction to architecture patterns
- Text summarization
- Lab 3: Text Summarization of Small Files
- Lab 4: Abstractive Text Summarization with Amazon Titan Using LangChain
- Chatbots
- Code generation
- Lab 5: Using Amazon Bedrock Models for Code Generation
- LangChain and agents for Amazon Bedrock
- Lab 6: Integrating Amazon Bedrock Models with LangChain Agents
