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How machines understand human intent has always been a subject of deep interest for me. Although I embarked on my journey into AI and machine learning in 2007, it was in early 2016 that I became fascinated by natural language processing (NLP), while building a virtual data analyst. When Google released BERT in 2018, I became convinced that NLP was on the brink of a revolution. In 2022, following the release of text-davinci-002, a model in OpenAI’s GPT-3 series, I decided to join Yarnit, a generative-AI-based content marketing platform, to build the AI backbone of the application. The mission was to create a platform where enterprise content marketing teams could generate marketing assets—social media posts, blogs, emails, and more—at high speed, large scale, and lower cost, with greater accuracy. It quickly became apparent that no generative model could achieve this effectively without incorporating brand-specific knowledge and access to proprietary data. This realization led me to explore retrieval-augmented generation (RAG). Large language models (LLMs) often fail to meet user expectations. While they are incredibly effective at storing and generating knowledge, they are also prone to hallucinations— confident yet incorrect outputs. This is where RAG provides a breakthrough, allowing LLMs to retrieve relevant, real-time, and factual information before generating responses. The beauty of RAG lies in its simplicity of concept combined with the nuance of implementation. The transformative potential of RAG in overcoming LLMs’ core limitations is what has kept both researchers and practitioners deeply engaged. When I began researching RAG, it was still a relatively unexplored area. Formal learning resources were scarce, and most knowledge was scattered across blogs, social media posts, research papers, and discussion forums. I shared many of my own findings on social platforms and in blog posts. Eventually, the idea of consolidating all these learnings into a comprehensive book took shape. With the goal of creating a simple, practical resource for technology professionals building LLM-based applications, I started working on this book in mid-2024. Over time, it has evolved into a foundational guide to RAG, covering both breadth and depth, while ensuring practical implementation through clear explanations and simple Python code. I firmly believe that RAG is an essential skill for anyone working with AI applications and that mastering it requires a solid conceptual foundation. This book is designed to provide just that. Writing it has been an incredibly enriching experience, and I have learned a great deal along the way. I hope you find it both enlightening and enjoyable.