Overview

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.

Full Product Details

9781633435858

Table of Contents

Reviews

""The book does a great job of deconstructing RAG and presenting it in digestible chunks."" Abhishek Gupta, Amazon Web Services   ""A good resource for beginners and an excellent refresher for experienced people."" Naga Santhosh Reddy Vootukuri, Senior Software Engineering Manager, Microsoft   ""I can highly recommend this book. Complex topics are broken down into small and easy to understand pieces."" Bert Gollnick, Data Scientist, Gollnick Data Solutions

Author Information

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.

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