about this book
Retrieval-augmented generation (RAG) is transforming the landscape of applied generative AI. First introduced by Lewis and colleagues in their seminal paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” (https://arxiv.org/abs/2005.11401), RAG has quickly become a cornerstone of modern AI, enhancing the reliability and trustworthiness of large language models (LLMs).
A Simple Guide to Retrieval Augmented Generation is a foundational guide for individuals looking to explore RAG. It offers a gentle, yet comprehensive introduction to the concept, along with practical insights helpful in using RAG to their advantage.
Who should read this book?
This book is for technology professionals who want to be introduced to the concept of RAG and build LLM-based apps. It is a handy book for both beginners and experienced professionals alike. If you’re a data scientist, data engineer, ML engineer, software developer, technology leader, or student interested in generative-AI-powered application development, you will find this book valuable. Upon completing this book, you can expect to