Retrieval Augmented Generation (RAG) enhances a Large Language Model (LLM) by adding context from an external knowledge base. This allows the model to provide responses based on proprietary content, recent information, or specific data sets. This book covers: The components of a RAG system Creating a RAG knowledge base The indexing and generation pipeline Evaluating a RAG system Advanced RAG strategies RAG tools, technologies, and frameworks The content progresses from basic RAG indexing and generation pipelines to modular RAG and multimodal data applications involving images and spreadsheets. About the Technology Retrieval Augmented Generation is an architectural approach where a model retrieves relevant information from knowledge stores—such as search indices, vector databases, or document sets—and generates an answer using the user’s prompt and the retrieved material as context. This process is used to mitigate hallucinations and constrain the model's output to specific source material. About the Book This guide provides a technical introduction to RAG using Python code examples. It covers concepts from initial implementation to advanced approaches, utilizing tools such as LangChain and various Python libraries. What’s Inside RAG components and applications Evaluating RAG systems Tools and frameworks for implementing RAG About the Readers For data scientists, engineers, and technology managers. No prior LLM experience is required. Examples are provided in annotated Python code. About the Author Abhinav Kimothi is a professional in data science, machine learning, and AI, currently working as a Director of Data Science at Sigmoid. Table of Contents Part 1 1 LLMs and the need for RAG 2 RAG systems and their design Part 2 3 Indexing pipeline: Creating a knowledge base for RAG 4 Generation pipeline: Generating contextual LLM responses 5 RAG evaluation: Accuracy, relevance, and faithfulness Part 3 6 Progression of RAG systems: Naïve, advanced, and modular RAG 7 Evolving RAGOps stack Part 4 8 Graph, multimodal, agentic, and other RAG variants 9 RAG development framework and further exploration