Overview

What happens when an AI system answers confidently-and the knowledge it relied on was incomplete, outdated, or misassembled? Retrieval-Augmented Generation Patterns is written for builders who have learned the hard way that RAG is not a prompt trick, a vector database add-on, or a framework choice. It is an architectural commitment. Once deployed, RAG systems expose uncomfortable realities about truth, authority, latency, governance, and responsibility-realities that demos politely ignore.This book treats RAG as a systems discipline. It examines how context flows through pipelines, how retrieval decisions compound downstream, and how probabilistic reasoning collides with production expectations. Instead of tool-centric recipes, it presents enduring patterns: designs that survive scale, audits, failures, and organizational change. You'll encounter questions most teams postpone until it's too late-and patterns that help answer them before incidents occur. Inside, you'll explore: Why retrieval quality matters more than model choice How context assembly quietly determines correctness Where evaluation breaks down in probabilistic systems How latency, cost, and accuracy trade against each other Why governance is a technical concern, not a legal afterthought How RAG failures propagate across distributed systems What alignment means when knowledge lives outside the model This is not a book for experimentation theater. It is for engineers, architects, and technical leaders responsible for systems that must explain themselves under pressure. If you are moving from prototypes to products-or cleaning up a RAG system already in production-this book gives you the patterns to reason clearly, design deliberately, and accept accountability with eyes open. If your AI systems must behave consistently, justify their answers, and remain adaptable over time, this book belongs on your desk-not your wishlist.

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