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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
Transform Large Language Models into Intelligent Agents That Reason, Retrieve, and Reflect
Large language models can generate text-but intelligence requires more than words.
True intelligence demands reasoning, memory, and reflection. It requires systems that can connect what they know, retrieve what they need, and learn from what they produce.
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.
What You'll Learn
Key Features
This book is written for:
You don't need a supercomputer-just intermediate Python skills, a working knowledge of APIs, and curiosity. Every example can be run on a standard laptop or cloud environment.
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