Developing Generative AI Applications on AWS

In this course, you will learn to do the following:

• Develop generative AI applications using Amazon Bedrock.
• Design architecture patterns of generative AI applications.
• Configure Amazon Bedrock APIs to invoke foundation models (FMs) programmatically.
• Develop agentic AI applications by integrating Amazon Bedrock tools and open source

frameworks.

• Build custom solutions with Retrieval Augmented Generation (RAG) and Amazon Bedrock

Knowledge Bases.

• Integrate open-source SDKs with Amazon Bedrock to build business.
• Optimize model responses by applying prompt engineering techniques.
• Evaluate generative AI application components.
• Implement responsible AI practices to protect generative AI.

This course is intended for software developers.
 

We recommend that attendees of this course have:
• Completed the Generative AI Essentials AWS instructor-led course
• Intermediate-level proficiency in Python
• Familiarity with AWS Cloud

Day 1 Module 1: Exploring Components of Generative AI Applications on AWS

• Understanding generative AI concepts
• Identifying AWS generative AI stack components
• Designing generative AI application components

Module 2: Programming with Amazon Bedrock

• Guiding model response generation
• Using Amazon Bedrock programmatically

Hands-on lab: Develop with Amazon Bedrock APIs Hands-on lab: Develop Streaming Patterns with Amazon Bedrock APIs Module 3: Applying Prompt Engineering for Developers

• Introducing prompt engineering
• Introducing prompt techniques
• Optimizing prompts for better results

Module 4: Using Amazon Bedrock APIs in Common Architectures

• Implementing architecture patterns with Amazon Bedrock APIs
• Exploring common use cases
• Adding conversational memory to extend context

Hands-on lab: Develop Conversation Patterns with Amazon Bedrock APIs Module 5: Customizing Generative AI Responses with RAG

• Implementing Retrieval Augmented Generation (RAG)
• Using Amazon Bedrock Knowledge Bases

Hands-on lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases Module 6: Integrating Open-Source Frameworks with Amazon Bedrock

• Invoking a foundation model in Amazon Bedrock using LangChain
• Using LangChain for context-aware responses

Hands-on lab: Develop a Generative AI Application Pattern using Open-Source Frameworks and Amazon Bedrock Knowledge Bases Day 2 Module 7: Evaluating Generative AI Application Components

• Evaluating application components
• Evaluating model output
• Evaluating RAG output
• Optimizing latency and cost

Hands-on lab: Evaluating Retrieval Augmented Generation (RAG) Applications Module 8: Implementing Responsible AI

• Understanding responsible AI
• Mitigating bias and addressing prompt misuses
• Using Amazon Bedrock Guardrails

Hands-on lab: Securing Generative AI Applications Using Bedrock Guardrails Module 9: Using Tools and Agents in Generative AI Applications

• Using tools
• Understanding AI agents
• Understanding open-source agentic frameworks
• Understanding agent interoperability

Module 10: Developing Amazon Bedrock Agents

• Implementing Amazon Bedrock Flows
• Designing Amazon Bedrock Agents
• Developing Amazon Bedrock Inline Agents
• Designing multi-agent collaboration
• Using Amazon Bedrock AgentCore

Hands-on lab: Developing Amazon Bedrock Agents Integrated with Amazon Bedrock Knowledge Bases and Guardrails