Building LLM Agents with RAG, Knowledge Graphs & Reflection / Najlacnejšie knihy
Building LLM Agents with RAG, Knowledge Graphs & Reflection

Kód: 50594905

Building LLM Agents with RAG, Knowledge Graphs & Reflection

Autor Mira S. Devlin

Transform Large Language Models into Intelligent Agents That Reason, Retrieve, and ReflectIn Building LLM Agents with RAG, Knowledge Graphs & Reflection, AI systems architect Mira S. Devlin guides you beyond the surface of generat ... celý popis

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Anotácia knihy

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.


What You'll Learn

  1. The Cognitive Core of AI Agents

    • Understand the architecture of transformers, tokenization, and attention.

    • Explore the shift from static LLMs to adaptive, outcome-driven agents.

    • Learn how retrieval, reflection, and reasoning form the four pillars of intelligence.

  2. Retrieval-Augmented Generation (RAG)

    • Implement retrievers, rankers, and generators using open-source frameworks.

    • Evaluate accuracy with metrics like Recall@K, Precision@K, and grounding quality.

    • Build a working RAG-powered knowledge bot capable of live data integration.

  3. Knowledge Graphs and Structured Reasoning

    • Design and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG.

    • Represent relationships between data entities for context-rich reasoning.

    • Combine structured knowledge with unstructured language for explainable AI.

  4. Reflection and Cognitive Loops

    • Implement Plan → Act → Reflect → Revise cycles for self-improving intelligence.

    • Explore short-term and long-term memory systems for continuous learning.

  5. Multi-Agent Collaboration

    • Architect intelligent teams of agents that can plan, delegate, and verify results.

    • Understand communication protocols, cooperative memory, and role specialization.

    • Use frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination.

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.

Key Features:About the Author:

Mira S. Devlin is an AI systems architect specializing in the intersection of language models, retrieval pipelines, and knowledge reasoning frameworks.

Who This Book Is For:
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