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