Building LLM Agents with RAG, Knowledge Graphs by Mira S. Devlin(.PDF)
File Size: 10 MB
Building LLM Agents with RAG, Knowledge Graphs & Reflection: A Practical Guide to Building Intelligent, Context-Aware, and Self-Improving AI Agents by Mira S. Devlin
Requirements: .ePUB, .PDF, .MOBI/.AZW reader, 10 MB
Overview: Transform Large Language Models into Intelligent Agents That Reason, Retrieve, and Reflect. In Building LLM Agents with RAG, Knowledge Graphs & Reflection, AI systems architect Mira S. Devlin guides you beyond the surface of generative AI into the world of agentic intelligence—where LLMs evolve from reactive tools into dynamic collaborators capable of grounding responses in truth, understanding context, and improving over time. This book doesn’t just explain concepts—it helps you build them. Each chapter blends theory, diagrams, and applied examples to show how retrieval, reasoning, and reflection interact inside modern AI agents. Whether you’re constructing a self-updating research assistant or a multi-agent workflow, you’ll gain a deep understanding of how today’s most advanced cognitive systems are designed. This book begins at the foundations: how transformers think and why LLMs are limited by static training data. It then progresses through retrieval-augmented generation (RAG), knowledge graphs, and reflective reasoning — culminating in the architecture of multi-agent collaboration. Each chapter concludes with an “Agent in Action” section—hands-on projects and guided workflows that turn abstract concepts into working systems you can build, extend, and deploy. A basic familiarity with Python programming and Machine Learning concepts will be useful, though not strictly required.
Genre: Non-Fiction > Tech & Devices
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