A Simple Guide to Retrieval Augmented Generation

by Abhinav Kimothi

Everything you need to know about Retrieval Augmented Generation in one human-friendly guide.

Augmented Generation—or RAG—enhances an LLM’s available data by adding context from an external knowledge base, so it can answer accurately about proprietary content, recent information, and even live conversations. RAG is powerful, and with A Simple Guide to Retrieval Augmented Generation, it’s also easy to understand and implement!

In A Simple Guide to Retrieval Augmented Generation you’ll

• The components of a RAG system
• How to create a RAG knowledge base
• The indexing and generation pipeline
• Evaluating a RAG system
• Advanced RAG strategies
• RAG tools, technologies, and frameworks

A Simple Guide to Retrieval Augmented Generation gives an easy, yet comprehensive, introduction to RAG for AI beginners. You’ll go from basic RAG that uses indexing and generation pipelines, to modular RAG and multimodal data from images, spreadsheets, and more.

About the Technology

If you want to use a large language model to answer questions about your specific business, you’re out of luck. The LLM probably knows nothing about it and may even make up a response. Retrieval Augmented Generation is an approach that solves this class of problems. The model first retrieves the most relevant pieces of information from your knowledge stores (search index, vector database, or a set of documents) and then generates its answer using the user’s prompt and the retrieved material as context. This avoids hallucination and lets you decide what it says.

About the Book

A Simple Guide to Retrieval Augmented Generation is a plain-English guide to RAG. The book is easy to follow and packed with realistic Python code examples. It takes you concept-by-concept from your first steps with RAG to advanced approaches, exploring how tools like LangChain and Python libraries make RAG easy. And to make sure you really understand how RAG works, you’ll build a complete system yourself—even if you’re new to AI!

What’s Inside

• RAG components and applications
• Evaluating RAG systems
• Tools and frameworks for implementing RAG

About the Readers

For data scientists, engineers, and technology managers—no prior LLM experience required. Examples use simple, well-annotated Python code.

About the Author

Abhinav Kimothi is a seasoned data and AI professional. He has spent over 15 years in consulting and leadership roles in data science, machine learning and AI, and currently works as a Director of Data Science at Sigmoid.

Table of Contents

Part 1
1 LLMs and the need for RAG
2 RAG systems and their design
Part 2
3 Indexing Creating a knowledge base for RAG
4 Generation Generating contextual LLM responses
5 RAG Accuracy, relevance, and faithfulness
Part 3
6 Progression of RAG Naïve, advanced, and modular RAG
7 Evolving RAGOps stack
Part 4
8 Graph, multimodal, agentic, and other RAG variants
9 RAG development framework and further exploration

Genres: Artificial Intelligence

256 pages, Paperback

Published July 15, 2025

Reviews

[Bert Gollnick · Rating 5 out of 5]

Navigating concepts like Retrieval-Augmented Generation (RAG), can be quite difficult. That is why I consider "A Simple Guide to Retrieval Augmented Generation" a brilliant beacon of clarity. If you want to genuinely understand and even implement RAG, this book is an absolute must-read.

What truly struck me is the author's remarkable ability to distill profoundly complex topics into simple, digestible terms. Technical concepts that usually require multiple re-reads became intuitively clear on the very first pass. That, for me, was a game-changer.

The author masterfully navigates RAG's nuances, breaking down its architecture and applications with an engaging style.

Beyond its impressive teaching approach, I found the book to be a truly practical resource. It is more than just a book; it's an invaluable educational tool. It's a clear testament to the author's deep expertise and exceptional communication talent. For anyone intimidated by modern AI, or simply seeking the clearest explanation of RAG, this guide is an indispensable addition. It is, without a doubt, an outstanding achievement.

[Scott Pearson · Rating 5 out of 5]

Retrieval-augmented generation (RAG) is a process in artificial intelligence (AI) where specific documents can be consulted to provide an answer. It provides a way for individual groups to customize a bot for their internal uses. For example, a company can provide business policies specific to their own organization. Or healthcare firms can provide literature up to date with their own fields, not just the latest AI build of the foundation model.

Frankly, this topic confused me the most in all the presentations I've read about AI. Finally, this book explained it in a way that I now feel comfortable implementing a solution. A lot of books provide Python code that will do it for you, but an absence of theory avoids expert customization. I work in PHP via a REST API, not Python, so I need to understand concepts, not just code. I appreciate this book's clarity.

Now, I can explain the concepts to my engineering colleagues who also need to understand how things work, not just how to make things work. The graphics fill my slide decks to explain the entire process. Thankfully, the publisher provides a free eBook so that electronic copies of illustrations are easy to access and use. I finally feel like I'm able to explain how RAG works to them - my ultimate goal in reading several books about the topic.

Obviously, books like this are for a niche audience, but technical folk interested in understanding the theory, not just the code, will be ready to work in a wide variety of contexts. AI is becoming a core competency among software developers these days, and a solid theoretical understanding is becoming as essential as web development or using the cloud. This book will help those interested in RAG for contextualization. They will benefit from giving it a deep dive.

[Harshwardhan · Rating 5 out of 5]

Absolutely loved this book! As someone who uses RAG applications every day, I found it to be an incredibly practical and clear guide. The explanations are easy to follow, and the diagrams make even complex concepts simple to grasp. I keep coming back to this book as both a reference and a teaching resource. Highly recommend it to anyone looking to truly understand RAG from the ground up whether you’re a beginner or experienced practitioner. Thank you, Abhinav, for creating such a helpful resource!

[Alejandro Cuevas]

This book offers an excellent introduction to the world of Generation Augmented Retrieval (GAR), a technology that has constantly evolved and continues to incorporate new implementation methods. It is especially useful for those looking to develop AI-based chatbot systems and need to minimize model-generated hallucinations. It is definitely a highly recommended read for anyone getting started in this innovative field.

[Abhishek Gupta · Rating 5 out of 5]

I have worked on RAG systems and I found this valuable because it goes beyond the "basics" and tackles practical, real-world topics. At the same time, it can be used as a refresher/reference as well since it's very well structured. Don't be fooled by the "Simple" in the title - it is fairly exhaustive, but explained in clear, organized (and hence, simple) way.

[Shan P S · Rating 5 out of 5]

Excellent introductory book. Easy to read and well organized. The book not only introduces the RAG framework, but also shows future path for learning.

[Khan · Rating 5 out of 5]

4.6

[cynthia · Rating 4 out of 5]

read half of it and it was enough. helped me understand how RAG works in theory as a complete beginner. unfortunately, the code was very confusing for me since it wasn't really explained