welcome

Retrieval Augmented Generation, or RAG, has emerged as a pivotal technique in the realm of applied generative AI, offering reliability and trustworthiness in the outputs from Large Language Models (LLMs). This foundational guide is aimed at enthusiasts, practitioners and leaders who are looking for an easy yet comprehensive introduction to RAG. While prior exposure to the world of machine learning, generative AI and Large Language Models (LLMs) is always helpful, this book is a foundational guide and does not assume that you have a deep understanding of the concepts. You will also gain some perspective of the Large Language Models in the first chapter, itself.

I started working in the Natural Language Processing domain in 2016 and when OpenAI released GPT-3 in 2020, it was nothing short of magical. However, as a practitioner of AI and NLP, the limitations of the technology were evident. Hallucinations and memory limitations emerged to be two big hurdles in the implementation of LLMs. That is when I discovered RAG and it proved to be a game-changer. Since then, I have been tracking, experimenting with and building applications leveraging the advancements in RAG. Like with any new technique, RAG comes with a bit of a learning curve. With this book, I have tried to condensed my learnings of the last few years for you to get a solid foundation of RAG.