Course Overview
TOPIn this advanced two-day course, software developers learn to build and customize AI solutions by using Amazon Bedrock programmatically. Through hands-on exercises and labs, participants will invoke foundation models through Amazon Bedrock APIs, implement Retrieval Augmented Generation (RAG) patterns with Amazon Bedrock Knowledge Bases, and develop AI agents with tool integration. The course focuses on the practical implementation of prompt engineering techniques, responsible AI practices with Amazon Bedrock Guardrails, open source framework integration, and architectural patterns for real-world business applications.
Scheduled Classes
TOP| 03/10/26 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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| 03/30/26 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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| 05/05/26 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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| 05/26/26 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOPDevelop 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.
- In this course, you will learn to do the following:
Outline
TOPImplementing 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
- Understanding generative AI concepts
- Identifying AWS generative AI stack components
- Designing generative AI application components
- 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
- Introducing prompt engineering
- Introducing prompt techniques
- Optimizing prompts for better results
- 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
- Implementing Retrieval Augmented Generation (RAG)
- Using Amazon Bedrock Knowledge Bases
- Hands-on lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases
- 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
- Evaluating application components
- Evaluating model output
- Evaluating RAG output
- Optimizing latency and cost
- Hands-on lab: Evaluating Retrieval Augmented Generation (RAG) Applications
- Understanding responsible AI
- Mitigating bias and addressing prompt misuses
- Using Amazon Bedrock Guardrails
- Hands-on lab: Securing Generative AI Applications Using Bedrock Guardrails
- Using tools
- Understanding AI agents
- Understanding open source agentic frameworks
- Understanding agent interoperability
Prerequisites
TOPCompleted the Generative AI Essentials AWS instructor-led course
- Intermediate-level proficiency in Python
- Familiarity with AWS Cloud
- We recommend that attendees of this course have:
Who Should Attend
TOPThis course is intended for software developers.
