{"product_id":"building-llm-agents-with-rag-mira-s-devlin-9798232017378","title":"Building LLM Agents with RAG, Knowledge Graphs \u0026 Reflection: A Practical Guide to Building Intelligent, Context-Aware, and Self-Improving AI Agent","description":"\u003cp\u003e\u003cstrong\u003eBuilding LLM Agents with RAG, Knowledge Graphs \u0026amp; Reflection\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eA Practical Guide to Building Intelligent, Context-Aware, and Self-Improving AI Agents\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003eBy Mira S. Devlin\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eTransform Large Language Models into Intelligent Agents That Reason, Retrieve, and Reflect\u003c\/p\u003e\u003cp\u003eLarge language models can generate text-but intelligence requires more than words.\u003cbr\u003eTrue 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.\u003c\/p\u003e\u003cp\u003eIn \u003cem\u003eBuilding LLM Agents with RAG, Knowledge Graphs \u0026amp; Reflection\u003c\/em\u003e, 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.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eThis 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.\u003c\/em\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You'll Learn\u003c\/strong\u003e\u003c\/p\u003e\u003col\u003e\n\u003cli\u003eThe Cognitive Core of AI Agents\u003cul\u003e\n\u003cli\u003eUnderstand the architecture of transformers, tokenization, and attention.\u003c\/li\u003e\n\u003cli\u003eExplore the shift from static LLMs to adaptive, outcome-driven agents.\u003c\/li\u003e\n\u003cli\u003eLearn how retrieval, reflection, and reasoning form the four pillars of intelligence.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003eRetrieval-Augmented Generation (RAG)\u003cul\u003e\n\u003cli\u003eMaster the techniques that make models factually grounded and transparent.\u003c\/li\u003e\n\u003cli\u003eImplement retrievers, rankers, and generators using open-source frameworks.\u003c\/li\u003e\n\u003cli\u003eEvaluate accuracy with metrics like Recall@K, Precision@K, and grounding quality.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003eKnowledge Graphs and Structured Reasoning\u003cul\u003e\n\u003cli\u003eDesign and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG.\u003c\/li\u003e\n\u003cli\u003eCombine structured knowledge with unstructured language for explainable AI.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003eReflection and Cognitive Loops\u003cul\u003e\n\u003cli\u003eBuild agents that evaluate their own outputs and correct themselves.\u003c\/li\u003e\n\u003cli\u003eImplement Plan → Act → Reflect → Revise cycles for self-improving intelligence.\u003c\/li\u003e\n\u003cli\u003eExplore short-term and long-term memory systems for continuous learning.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003eMulti-Agent Collaboration\u003cul\u003e\u003cli\u003eUse frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination.\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eEnd-to-end coverage: From LLM fundamentals to advanced RAG and reflection architectures.\u003c\/li\u003e\n\u003cli\u003ePractical code labs: Step-by-step walkthroughs in Python with modular components.\u003c\/li\u003e\n\u003cli\u003eVisual clarity: Concept diagrams, data flow maps, and evaluation schematics throughout.\u003c\/li\u003e\n\u003cli\u003eDebugging insights: Identify hallucinations, reasoning gaps, and retrieval errors with real-world examples.\u003c\/li\u003e\n\u003cli\u003eScalable design patterns: Extend single-agent models into multi-agent collaborative systems.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThis book is written for: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eAI developers, data scientists, and engineers who want to move beyond simple LLM prompts.\u003c\/li\u003e\n\u003cli\u003eArchitects and product innovators building intelligent, explainable, and adaptive AI systems.\u003c\/li\u003e\n\u003cli\u003eResearchers and students seeking a structured understanding of retrieval-based reasoning and reflection.\u003c\/li\u003e\n\u003cli\u003eTech leaders and educators integrating agentic AI into enterprise or academic environments.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eYou 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.\u003c\/p\u003e\u003cp\u003eOrder Now.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Mira S. Devlin\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798232017378\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Richa Publishing Minds\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 11\/09\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 278\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.06lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.25h x 7.50w x 0.58d","brand":"Mira S. 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