Agentic RAG

Building Self-Correcting Knowledge Systems

About the Book

This book is a comprehensive, hands-on guide to designing, building, and deploying Agentic RAG systems. It moves beyond traditional, static information retrieval to explore the new frontier of self-correcting, autonomous knowledge systems powered by Large Language Models. By the end of this book, you will not just understand Agentic RAG; you will have built it. You will possess the practical skills to create robust, self-correcting knowledge systems that can reason, adapt, and provide a level of accuracy and reliability that was previously unattainable.


Philosophy

The core philosophy of this book is "learning by doing." It is intentionally designed to be a practical, implementation-focused resource rather than a dense theoretical treatise. I believe the best way to understand complex systems is to build them. Every chapter introduces a core concept and immediately follows up with hands-on exercises and code examples to solidify that knowledge. The book starts with simple, foundational blocks and progressively combines them to construct sophisticated, real-world applications. The algorithms and code are presented in the simplest possible manner, assuming a beginner's starting point but building to an advanced level of mastery.


Key Features

1. Step-by-Step Practical Guides: Clear, numbered instructions for building everything from a basic RAG pipeline to a multi-tool, self-reflecting agent.
2. Focus on the Agentic Loop: In-depth exploration of the Plan-Retrieve-Assess-Generate cycle that forms the core of an intelligent RAG system.
3. Self-Correction Techniques: Practical implementations of methods for an agent to detect poor retrieval quality and automatically refine its queries.
4. Multi-Tool Integration: Hands-on tutorials for equipping agents with diverse tools, including vector search, SQL databases, and live web search APIs.
5. Ready-to-Use Code: All code is provided with detailed explanations, designed to be easily understood, adapted, and integrated into your own projects.
6. End-to-End Project: A final chapter dedicated to a complete DIY project, including setup, coding, and deployment, to synthesize all learned concepts.
7. Comprehensive Coverage: Discusses design, architecture, components, implementation, deployment, and future scope in a clear and accessible manner.


To Whom This Book Is For

This book is written for a broad audience, catering to both beginners and advanced learners:

1. B.Tech/M.Tech Computer Science Students: An ideal textbook or supplementary resource that provides practical, project-based learning on a cutting-edge AI topic, perfectly aligned with modern curricula.
2. AI/ML Developers and Engineers: A practical guide for professionals looking to upgrade their skills and build more robust, reliable, and intelligent LLM-powered applications.
3. Data Scientists: A resource for learning how to ground analytical models in external knowledge bases, enhancing the accuracy and explainability of their outputs.
4. Researchers and Academics: A practical implementation guide to complement theoretical research in AI, agents, and information retrieval.

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Kindly go through the table of contents and refer kindle edition for a glance on the related contents.

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