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RETRIEVAL-AUGMENTED GENERATION: PRODUCTION PATTERNS : Building Reliable, Scalable, and Trustworthy AI Systems in the Real World
Retrieval-Augmented Generation (RAG) works in demos.
But production breaks everything.
This book is the definitive, real-world guide to building RAG systems that actually survive production.
Retrieval-Augmented Production Patterns goes far beyond prompts and vector databases. It shows how modern RAG systems fail, drift, leak data, lose trust, and collapse under scale—and how to design them to endure.
You’ll learn how
Design enterprise-grade RAG architectures
Prevent hallucinations, data leakage, and silent failures
Measure retrieval quality, faithfulness, and trust
Apply RAG in healthcare, finance, compliance, and developer platforms
Build agentic, graph-based, and reasoning-aware RAG systems
Govern knowledge, audits, access control, and ethics
Evolve RAG into a living, self-improving knowledge system
This is not a tutorial.
This is a production playbook.
If you are building AI systems that people must trust—this book is your blueprint.
But production breaks everything.
This book is the definitive, real-world guide to building RAG systems that actually survive production.
Retrieval-Augmented Production Patterns goes far beyond prompts and vector databases. It shows how modern RAG systems fail, drift, leak data, lose trust, and collapse under scale—and how to design them to endure.
You’ll learn how
Design enterprise-grade RAG architectures
Prevent hallucinations, data leakage, and silent failures
Measure retrieval quality, faithfulness, and trust
Apply RAG in healthcare, finance, compliance, and developer platforms
Build agentic, graph-based, and reasoning-aware RAG systems
Govern knowledge, audits, access control, and ethics
Evolve RAG into a living, self-improving knowledge system
This is not a tutorial.
This is a production playbook.
If you are building AI systems that people must trust—this book is your blueprint.
360 pages, Kindle Edition
Expected publication February 19, 2026
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