Building LLM Agents with RAG, Knowledge Graphs & Reflection: A Practical Guide to Building Intelligent, Context-Awar, (Paperback)

<b>Transform Large Language Models into Intelligent Agents That Reason, Retrieve, and Reflect</b><p>In <i>Building LLM Agents with RAG, Knowledge Graphs &amp; Reflection</i>, 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.</p><p>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.</p><br><b>What You'll Learn</b><ol><li><p>The Cognitive Core of AI Agents</p><ul><li><p>Understand the architecture of transformers, tokenization, and attention.</p></li><li><p>Explore the shift from static LLMs to adaptive, outcome-driven agents.</p></li><li><p>Learn how retrieval, reflection, and reasoning form the four pillars of intelligence.</p></li></ul></li><li><p>Retrieval-Augmented Generation (RAG)</p><ul><li><p>Implement retrievers, rankers, and generators using open-source frameworks.</p></li><li><p>Evaluate accuracy with metrics like Recall@K, Precision@K, and grounding quality.</p></li><li><p>Build a working RAG-powered knowledge bot capable of live data integration.</p></li></ul></li><li><p>Knowledge Graphs and Structured Reasoning</p><ul><li><p>Design and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG.</p></li><li><p>Represent relationships between data entities for context-rich reasoning.</p></li><li><p>Combine structured knowledge with unstructured language for explainable AI.</p></li></ul></li><li><p>Reflection and Cognitive Loops</p><ul><li><p>Implement Plan &amp;#8594; Act &amp;#8594; Reflect &amp;#8594; Revise cycles for self-improving intelligence.</p></li><li><p>Explore short-term and long-term memory systems for continuous learning.</p></li></ul></li><li><p>Multi-Agent Collaboration</p><ul><li><p>Architect intelligent teams of agents that can plan, delegate, and verify results.</p></li><li><p>Understand communication protocols, cooperative memory, and role specialization.</p></li><li><p>Use frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination.</p></li></ul></li></ol><p>Each chapter concludes with a</p>

• Building LLM Agents with RAG, Knowledge Graphs & Reflection: A Practical Guide to Building Intelligent, Context-Awar, (Paperback)
• Author: Independently Published
• ISBN: 9798273159013
• Format: Paperback
• Publication Date: 2025-11-05
• Page Count: 316