A Simple Guide to Retrieval Augmented Generation

Format:

Sound recording

Author/Creator:

Kimothi, Abhinav

Language:

Undetermined

Subjects (All):

Natural language processing (Computer science).

Artificial intelligence.

Physical Description:

1 online resource (1 audio file)

Place of Publication:

Manning Publications 2025

Summary:

Everything you need to know about Retrieval Augmented Generation in one human-friendly guide. 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 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 Reader 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. Quotes Essential read if you're serious about deploying factual, scalable, and future-ready AI systems. - Bhavishya Pandit, IBM A blend of expert advice, real-world examples, and use cases helping you navigate the complexities of Generative AI. - Naga Santhosh Reddy Vootukuri, Microsoft Offers clear explanations, solid foundations, and practical examples that truly make a difference. - Mr̀cio F. Nogueira, RankMyApp Insightful, practical, and timely! You'll walk away informed, inspired, and ready to build! - Tojin T. Eapen, Center for Creative Foresight.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1526826919