Practical Generative AI on AWS

GenAI is one of the most transformative technologies of our time, fundamentally changing how we build software and solve business problems. Unlike traditional AI that analyzes data to make predictions or classifications and usually requires training separate models for each specific task, GenAI provides powerful pre-trained models that can generate entirely new content from text and code to images and audio, while understanding context and responding to diverse prompts without additional training.

This book is your comprehensive guide to navigating AWS’s complete GenAI ecosystem and building intelligent applications that deliver real business value. It covers the spectrum of AWS GenAI services like Amazon Bedrock for managed foundation models, Amazon SageMaker JumpStart for flexible ML development, and Amazon Q for business and developer productivity. You will learn essential techniques from prompt engineering and systematic model evaluation to advanced implementations like RAG with Knowledge Bases that enhance responses with proprietary data. The book then advances to autonomous agents that can reason, plan, and execute complex workflows using both Amazon Bedrock’s managed agents and open-source agentic AI frameworks.

By the end of this book, you will have the knowledge, architectural patterns, and strategies needed to build GenAI solutions on AWS, whether you are just getting started with GenAI or building enterprise-scale multi-agent systems.

WHAT YOU WILL LEARN
● Implement real-world applications using Amazon Bedrock, Amazon SageMaker JumpStart, and Amazon Q.
● Implement advanced techniques like RAG, fine-tuning, and agentic AI.
● Master prompt engineering techniques and best practices.
● Develop secure and compliant GenAI solutions.
● Learn AWS best practices and responsible AI principles.
● Create end-to-end solutions integrating popular agentic AI frameworks.
● Build practical applications that solve real industry challenges.

WHO THIS BOOK IS FOR
This book is designed for software developers, AI/ML engineers, and solutions architects who want to build practical GenAI applications on AWS. It is ideal for developers with basic Python programming experience, a cloud engineer familiar with AWS services, or a technical professional transitioning into AI/ML roles.

1. Introduction to Generative AI and Foundation Models
2. Getting Started with Amazon Bedrock
3. Experimenting Foundation Models in Amazon Bedrock
4. Building Generative AI Applications with Amazon Bedrock APIs
5. Using Amazon Bedrock Knowledge Bases
6. Using Bedrock’s Managed Agents
7. Using Open-source Frameworks with Amazon Bedrock
8. Building Custom Models with Amazon Bedrock
9. Monitoring and Observability
10. Security and Responsible AI
11. RAG and Model Evaluation
12. Building Generative AI Assistant using Amazon Q
13. Getting Started with Generative AI on Amazon SageMaker JumpStart
14. Best Practices for Developing Generative AI Applications
15. Real-world GenAI

● Munish Dabra is a principal solutions architect at Amazon Web Services (AWS). He helps enterprises build generative AI (GenAI) and agentic systems that work reliably in production. Through his work with customers across diverse industries, Munish has gained extensive experience with the challenges of building GenAI applications at scale. He regularly shares these insights through technical blogs and speaking engagements at multiple AWS events. Known for his practical approach, Munish focuses on bridging the gap between GenAI potential and real-world implementation.

● Jay Rao is a principal solutions architect at AWS, specializing in AI/ML technologies. He has guided numerous customers through their GenAI journey, from foundational implementations to advanced AI systems, spanning prompt engineering, RAG pipelines, and multi-agent architectures, transforming proof-of-concepts into production-ready deployments. As the author of Computer Vision on AWS and a recognized thought leader in the AI space, Jay brings technical expertise with a unique ability to make complex concepts accessible. His practical experience and implementation strategies are reflected throughout this book. A frequent speaker at AWS events, Jay is known for demystifying advanced AI concepts and providing actionable guidance for successful AI adoption.

● KP Babu serves as a senior GenAI/ML specialist solution architect at AWS, specializing in enterprise GenAI implementation that balances cutting-edge innovation with responsible deployment practices. With extensive experience across diverse customer engagements, he has developed deep expertise in architecting GenAI systems that meet stringent security and compliance requirements. Babu is particularly focused on designing autonomous AI agents and orchestration frameworks that allow organizations to automate complex workflows while maintaining appropriate oversight. Through his technical publications and speaking engagements, he translates complex implementation challenges into practical guidance, consistently emphasizing that successful AI agents must reliably address genuine business problems by effectively reasoning, planning, and taking action within well-defined enterprise contexts.