Agentic RAG Essentials Crafting Autonomous AI with Retrieval-Enhanced Intelligence

Agentic RAG Essentials is your practical guide to building intelligent, autonomous systems using Retrieval-Augmented Generation (RAG). This book bridges the gap between modern AI theory and hands-on application, focusing on how to combine language models, dynamic memory, tool use, and multi-agent coordination into powerful AI agents. With real-world examples and clear explanations, you'll learn to design systems that think, plan, and act-making smarter decisions by grounding their output in facts.
Whether you're a developer, data scientist, or AI engineer, this book walks you through crafting autonomous agents that can retrieve relevant data, reason through complex tasks, and interact with APIs and users in meaningful ways. It demystifies cutting-edge RAG pipelines and agentic frameworks, so you can build solutions that are not only accurate but adaptive and scalable.
Summary:
This essential guide dives into the heart of agentic AI-where retrieval meets autonomy. Learn to create intelligent systems that combine LLMs with contextual search, tool integration, and long-term memory. From architectural blueprints to prompt engineering and multi-agent workflows, Agentic RAG Essentials equips you with everything you need to build the next generation of AI assistants.
Key Features of this Book:A complete breakdown of RAG pipelines, from retrievers to generatorsIn-depth exploration of agent planning, memory management, and tool useHands-on code examples using LangChain, LlamaIndex, and other leading frameworksStrategies for reducing hallucinations and improving output accuracyGuidance on enterprise use cases, ethical design, and security best practicesModular designs for building reusable, production-ready agentsTips on staying ahead in the fast-evolving agentic AI landscapeThis book is for developers, AI practitioners, ML engineers, and technical team leads eager to move beyond simple prompts and LLM queries. If you want to build AI that retrieves, thinks, and acts-not just responds-this book is for you. Prior Python knowledge and familiarity with LLM basics will help, but no deep ML background is required.

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