A Simple Guide to Retrieval Augmented Generation

Abhinav Kimoth

ISBN 10: 1633435857 / ISBN 13: 9781633435858

Published by Manning Publications Jul 2025, 2025

About this Item

Neuware - 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 learn: 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 pipeline: Creating a knowledge base for RAG 4 Generation pipeline: Generating contextual LLM responses 5 RAG evaluation: Accuracy, relevance, and faithfulness Part 3 6 Progression of RAG systems: 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 Get a free Elektronisches Buch (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Seller Inventory # 9781633435858

Bibliographic Details

• Title: A Simple Guide to Retrieval Augmented Generation
• Publisher: Manning Publications Jul 2025
• Publication Date: 2025
• Language: English
• ISBN 10: 1633435857
• ISBN 13: 9781633435858
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 231W x 185H x 19D millimeters 231 millimeters width by 185 millimeters height by 19 millimeters depth
• Weight: 350 grams

About this title

Synopsis

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

Generative AI models struggle when you ask them about facts not covered in their training data. Retrieval 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 learn:

• 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 shows you how to enhance an LLM with relevant data, increasing factual accuracy and reducing hallucination. Your customer service chatbots can quote your company's policies, your teaching tools can draw directly from your syllabus, and your work assistants can access your organization's minutes, notes, and files.

About the Author

Abhinav Kimothi is an entrepreneur and Vice President of Artificial Intelligence at Yarnit. He has spent over 15 years consulting and leadership roles in data science, machine learning and AI.