Learn RAG through pictures, not code.
Most RAG resources drown you in Python. This book teaches the ideas behind retrieval-augmented generation through clean diagrams, real-world analogies, and plain language. 22 techniques, one visual language, no code required.
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RAG is everywhere. Most explanations are terrible.
Blog posts assume you already know vector databases. Papers assume you read the last ten papers. Courses dump 800 lines of LangChain on you before explaining what an embedding is. This book starts at zero and stays visual the whole way: a familiar analogy makes the idea click, a custom illustration shows exactly how it works, and a short language-agnostic sketch locks it in.
22 techniques. One visual language.
From the foundational pipeline to graph retrieval and self-correcting systems, every stage of RAG, explained without a single line of code.
Foundations
- Simple RAG
- RAG for structured data
- Reliable RAG with verification
Smarter chunking
- Proposition chunking
- Contextual chunk headers
- Context enrichment windows
- Semantic chunking
Query strategies
- Query transformations
- Hypothetical document embedding (HyDE)
Retrieval & post-processing
- Contextual compression
- Document augmentation
- Fusion retrieval
- Reranking
- Hierarchical indices
- Dartboard retrieval
- Multi-modal RAG
Advanced & self-correcting
- Retrieval with feedback loops
- Adaptive retrieval
- Explainable retrieval
- Corrective RAG
- Graph RAG
Evaluation
- RAG evaluation
- Decision guide for choosing techniques
- Full glossary
Built for people who think in pictures
Engineers new to AI
Get the mental model first. Then any framework, LangChain, LlamaIndex, or your own, finally makes sense.
PMs & founders
Understand what your AI team is building and make better roadmap calls without learning Python.
Students & researchers
Skip the framework noise and focus on the algorithms that actually matter.
Why this book vs. everything else
| This Book | Blog Posts | Courses | Papers | |
|---|---|---|---|---|
| No code required | ||||
| Visual-first explanations | partial | |||
| Beginner-friendly | partial | |||
| 22 techniques in one place | ||||
| Tradeoff guidance | partial | partial |
Formats and updates
- PDF and EPUB, both included
- Reads on laptop, tablet, phone, or e-reader
- Instant download after checkout
- Free lifetime updates to future editions
- 250 pages of diagrams, analogies, and intuition
Written by the creator of RAG Techniques
Nir Diamant is an AI researcher and open-source educator. His GitHub repositories have earned over 75,000 stars and are used by more than 500,000 developers every month. This book distills his widely used RAG Techniques repository into a visual, code-free format. His DiamantAI Newsletter reaches 35,000+ subscribers and ranks in the top 0.1% on Substack.
Understand RAG deeply, today.
Stop wrestling with framework boilerplate. Get the intuition that transfers to any language, any stack, any new technique the field invents next.