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RAG Made Simple: The Complete Visual Guide to Retrieval-Augmented Generation
What if you could master every major RAG technique without reading a single line of code?
Retrieval-Augmented Generation (RAG) has become the most important pattern in applied AI, letting language models answer questions grounded in your own documents instead of guessing from memory. But the simple version only gets you so far. When retrieval fails, chunks miss context, or the model hallucinations despite having the right information, you need a deeper toolkit.
This book maps the full landscape. It covers 22 distinct RAG techniques, from the foundational pipeline through advanced strategies like graph-based retrieval, self-correcting systems, and adaptive query routing. Every technique is explained through intuition, visual analogies, and custom illustrations, not code. No imports. No framework lock-in. Just the ideas, taught so they transfer to any language or stack.
What you'll
How to split documents intelligently using semantic chunking, proposition chunking, and contextual headers
Query transformation strategies that fix vague questions before they reach the search engine
Post-retrieval techniques like reranking, contextual compression, and fusion retrieval
Advanced systems including corrective RAG, explainable retrieval, feedback loops, and Graph RAG
How to systematically evaluate retrieval quality, faithfulness, and answer relevance
A decision guide to help you pick the right technique for your specific problem
Who this book is
Developers, AI engineers, and technical product managers who want to understand RAG deeply. Not the API calls. Not the boilerplate. The underlying algorithms and the intuition behind when each technique is the right choice.
Based on the RAG Techniques open-source repository (75,000+ GitHub stars), used by hundreds of thousands of developers worldwide.
Retrieval-Augmented Generation (RAG) has become the most important pattern in applied AI, letting language models answer questions grounded in your own documents instead of guessing from memory. But the simple version only gets you so far. When retrieval fails, chunks miss context, or the model hallucinations despite having the right information, you need a deeper toolkit.
This book maps the full landscape. It covers 22 distinct RAG techniques, from the foundational pipeline through advanced strategies like graph-based retrieval, self-correcting systems, and adaptive query routing. Every technique is explained through intuition, visual analogies, and custom illustrations, not code. No imports. No framework lock-in. Just the ideas, taught so they transfer to any language or stack.
What you'll
How to split documents intelligently using semantic chunking, proposition chunking, and contextual headers
Query transformation strategies that fix vague questions before they reach the search engine
Post-retrieval techniques like reranking, contextual compression, and fusion retrieval
Advanced systems including corrective RAG, explainable retrieval, feedback loops, and Graph RAG
How to systematically evaluate retrieval quality, faithfulness, and answer relevance
A decision guide to help you pick the right technique for your specific problem
Who this book is
Developers, AI engineers, and technical product managers who want to understand RAG deeply. Not the API calls. Not the boilerplate. The underlying algorithms and the intuition behind when each technique is the right choice.
Based on the RAG Techniques open-source repository (75,000+ GitHub stars), used by hundreds of thousands of developers worldwide.
- GenresArtificial Intelligence
393 pages, Kindle Edition
Published April 6, 2026
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April 30, 2026
A step-by-step guideline to improve RAG pipelines
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