book-v1.0

After years of writing and teaching RAG techniques through this repository, I'm sharing something I've been working on alongside it: a companion book that turns the ideas behind this repo into a visual, code-free learning experience.

**RAG Made Simple: The Complete Visual Guide to Retrieval-Augmented Generation** is built around a simple idea. You can't build what you don't understand. Every technique is taught through clear visual diagrams and plain-English analogies, so you grasp the *why* before you write a single line of code.

## What's inside

22 production-grade RAG techniques, grouped into four themes:

- **The foundations.** Simple RAG, reliable RAG, semantic chunking, contextual headers.
- **Smarter retrieval.** HyDE, query transformations, fusion retrieval, reranking, hierarchical indices.
- **Advanced architectures.** Corrective RAG, adaptive RAG, self-RAG with feedback loops, multi-modal RAG.
- **The cutting edge.** Graph RAG, agentic RAG, dartboard retrieval, explainable retrieval, RAG evaluation.

## Who this book is for

AI engineers, ML practitioners, software developers, and technical leaders who want to build RAG systems that actually work in production, without wading through hundreds of pages of boilerplate code.

If you've ever tried to follow a RAG tutorial and ended up more confused than when you started, this book exists for you. By the final chapter you'll understand 22 distinct techniques well enough to choose the right one, explain it to your team, and ship it.

## Launch pricing this week only

**RAG Made Simple is at $0.99 for a short time** while the launch window is open. Grab it now before the price goes up.

Get RAG Made Simple on Amazon: https://amzn.to/4cvxqSw

Also available on **Kindle Unlimited** for subscribers.

## Companion book also on countdown this week

The series prerequisite, **Prompt Engineering: Master the Art of AI Interaction from Zero to Hero**, is on a Kindle Countdown Deal at **$2.99 for 7 days** (April 15 through 21). If you want to strengthen your prompting foundations before working through the RAG techniques, this is the moment to grab it.

Get Prompt Engineering at countdown pricing: https://www.amazon.com/dp/B0DZ85RPB5?tag=diamantai-ragrm-20

## A personal thank you

This repo wouldn't exist in its current form without the community around it. Every star, every issue, every pull request, every email from an engineer who learned RAG from these notebooks shaped what this book became. Thank you for making this one of the most referenced RAG resources on GitHub, with over 26,000 stars and readers across the AI engineering community.

If you read the book and have feedback, whether technical errors, missing techniques, or anything that could be explained better, open an issue on this repo or reach out directly. I'd love to hear from you.

Nir Diamant