A Simple Guide to Retrieval Augmented Generation (Paperback)

Abhinav Kimoth

ISBN 10: 1633435857 / ISBN 13: 9781633435858

Editore: Manning Publications, New York, 2025

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Paperback. 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 Generationor RAGenhances 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 systemHow to create a RAG knowledge baseThe indexing and generation pipelineEvaluating a RAG systemAdvanced RAG strategiesRAG 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. Everything you need to know about Retrieval Augmented Generation in one human-friendly guide. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9781633435858

Dati bibliografici

• Titolo: A Simple Guide to Retrieval Augmented Generation (Paperback)
• Editore: Manning Publications, New York
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 1633435857
• ISBN 13: 9781633435858
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

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 eBook (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.

Informazioni sull?autore

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.