Mastering Amazon Bedrock: Advanced Generative-AI Solutions with Foundation Models, Textract, RAG, Agents, and the AWS AI Suite
Audience: Software engineers, ML engineers, data scientists, cloud and solutions architects, and AI product leaders seeking to build production-grade generative-AI solutions on AWS. The book provides actionable patterns, reference architectures, and business-focused insights for teams modernizing document workflows, launching conversational agents, or embedding AI across enterprise platforms.
Description: Mastering Amazon Bedrock is the definitive, hands-on guide to designing, building, and operating production-grade generative-AI solutions on AWS. This book takes you beyond the basics, teaching you how to select, customize, and operationalize foundation models; extract rich insights from documents with Textract; build Retrieval-Augmented Generation (RAG) knowledge bases; orchestrate multi-step AI agents; and embed Bedrock into the broader AWS ecosystem—all while meeting enterprise-grade security, compliance, cost, and MLOps requirements. Through deep technical dives, annotated Python examples, and real-world case studies, you’ll gain the expertise to deliver scalable, responsible, and future-proof AI applications.
Table of Contents
Chapter 01: The Generative-AI Revolution on AWS: Why Bedrock Matters
Objective: Introduce readers to the transformative potential of generative AI for enterprises, the unique role of Amazon Bedrock, and the foundational journey ahead.
Sections:
- Section 1.1: The Enterprise AI Landscape: Challenges and Opportunities
- Topics
- Generative AI use cases across industries
- Business drivers for adopting AI
- Enterprise challenges: scale, security, and compliance
- Analogy: AI as a new business utility
- Section 1.2: Amazon Bedrock in Context
- Topics
- What is Amazon Bedrock?
- How Bedrock simplifies foundation model access
- Bedrock vs. SageMaker, OpenAI, and other AI platforms
- The AWS AI suite ecosystem: Textract, Comprehend, Rekognition, and more
- Section 1.3: End-to-End AI Solution Patterns: From Idea to Production
- Topics
- Blueprints for document intelligence, conversational agents, and knowledge bases
- Overview of reference architectures
- Business implications: cost, agility, and risk mitigation
- Section 1.4: Key Ideas, Glossary, and Further Reading
- Topics
- Summary of key concepts introduced in this chapter
- Glossary of foundational terms
- Pointers to related chapters and external resources
- Section 1.5: Exercises and Actionable Next Steps
- Topics
- Reflect on AI opportunities in your organization
- Identify a sample use case for Bedrock
- Checklist: prerequisites for starting with AWS AI
Chapter 02: Amazon Bedrock Fundamentals: Architecture, Modes, and Getting Started
Objective: Equip readers with a foundational understanding of Bedrock’s architecture, operational modes, pricing, and essential concepts for building generative-AI applications, including hands-on onboarding guidance.
Sections:
- Section 2.1: Bedrock Architecture and Service Model
- Topics
- Managed API overview
- Runtime vs. agent modes
- Supported regions and service limits
- Integration points with other AWS services
- Section 2.2: Getting Started with Bedrock: Setup and Prerequisites
- Topics
- AWS account setup and prerequisites
- IAM roles and permissions for Bedrock
- Enabling Bedrock in your AWS environment
- Initial environment configuration and CLI setup
- Section 2.3: Prompt Engineering Essentials
- Topics
- Prompt structure and templates
- Context windows and tokenization
- Cost implications of prompt design
- Best practices for reproducibility
- Introduction to prompt caching (GA 2025): benefits and configuration
- Overview of prompt optimization: automated prompt rewriting for quality and efficiency
- Section 2.4: Pricing, Quotas, and Cost Management
- Topics
- Bedrock pricing models
- Token and throughput quotas
- Monitoring and controlling costs
- Business case: ROI of Bedrock vs. custom hosting
- Section 2.5: Key Ideas, Glossary, and Further Reading
- Topics
- Summary of Bedrock fundamentals
- Glossary of key terms: model, prompt, token, etc.
- Links to AWS documentation and next chapters