Retrieval-Augmented Generation

This book, "Retrieval-Augmented Generation," is conceived as a definitive guide for the next generation of engineers, data scientists, and AI practitioners. It is born from the recognition that the future of applied AI is not just about bigger models, but smarter systems. RAG represents this "smarter" approach, synergizing the generative power of LLMs with the factual accuracy of classical information retrieval.


Key Features:


1. NEP 2020 & AICTE Aligned: The book's pedagogy is rooted in the principles of the National Education Policy 2020. It prioritizes conceptual understanding, hands-on learning, and project-based assignments over rote learning, fostering a problem-solving mindset.
2. Globally Compatible Syllabus: While aligned with the Indian curriculum, the content covers universally fundamental concepts, making it perfectly suited for engineering and computer science courses at universities worldwide.
3. Lucid and Simple Language: Complex topics like transformers, vector embeddings, and agentic frameworks are broken down into intuitive, easy-to-understand concepts, supplemented with clear analogies and simple examples.
4. Progressive Learning Curve: The ten chapters are structured to take you on a logical journey from foundational knowledge of LLMs and vector databases to building your first simple RAG pipeline, and then mastering advanced optimization, evaluation, and deployment techniques.
5. Practical Code and Examples: The book is rich with practical code snippets and mini-projects, primarily using Python and industry-standard frameworks like LangChain and LlamaIndex, allowing you to immediately apply what you learn.
6. Focus on Evaluation: A dedicated chapter on evaluation and metrics equips you with the critical skills to measure the performance of your RAG systems, a topic often overlooked but vital for production-grade applications.
7. Ethics and Future Trends: The book concludes with a crucial discussion on the ethical implications, security challenges, and future frontiers of RAG, preparing you to be a responsible and forward-thinking AI architect.
8. End-of-Chapter Exercises and Projects: Each chapter includes a set of review questions, practical exercises, and project ideas to reinforce learning and test your understanding, making it ideal for both classroom use and self-assessment.


"Retrieval-Augmented Generation" is a comprehensive, practical, and forward-looking guide designed to equip B.Tech and M.Tech students with the essential skills to build the next generation of intelligent AI systems. In an era where Large Language Models (LLMs) are ubiquitous, this book focuses on the most critical challenge: making them trustworthy, factual, and relevant. It provides a complete roadmap to mastering Retrieval-Augmented Generation (RAG), the architectural pattern that grounds powerful LLMs in verifiable data.