Language: English
Published by The Institution of Engineering and Technology, 2023
ISBN 10: 1839537019 ISBN 13: 9781839537011
Seller: WorldofBooks, Goring-By-Sea, WS, United Kingdom
Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Condition: New.
Condition: New.
Condition: As New. Unread book in perfect condition.
Condition: As New. Unread book in perfect condition.
Condition: As New. Unread book in perfect condition.
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Language: English
Published by The MIT Press (edition ), 2021
ISBN 10: 0262045095 ISBN 13: 9780262045094
Seller: BooksRun, Philadelphia, PA, U.S.A.
Hardcover. Condition: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New.
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Seller: Rarewaves.com USA, London, LONDO, United Kingdom
US$ 31.52
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hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condition: new. Paperback. 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 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 LearnThe Cognitive Core of AI AgentsUnderstand 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.Retrieval-Augmented Generation (RAG)Implement retrievers, rankers, and generators using open-source frameworks.Evaluate accuracy with metrics like RecallatK, PrecisionatK, and grounding quality.Build a working RAG-powered knowledge bot capable of live data integration.Knowledge Graphs and Structured ReasoningDesign 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.Reflection and Cognitive LoopsImplement Plan Act Reflect Revise cycles for self-improving intelligence.Explore short-term and long-term memory systems for continuous learning.Multi-Agent CollaborationArchitect 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: End-to-end coverage: From LLM fundamentals to advanced RAG and reflection architectures.Framework-agnostic examples: Concepts applicable to GPT, Claude, Gemini, and open-source models.Practical code labs: Step-by-step walkthroughs in Python with modular components.Visual clarity: Concept diagrams, data flow maps, and evaluation schematics throughout.Debugging insights: Identify hallucinations, reasoning gaps, and retrieval errors with real-world examples.Scalable design patterns: Extend single-agent models into multi-agent collaborative systems.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: AI developers, data scientists, and engineers who want to move beyond simple LLM prompts.Architects and product innovators building intelligent, explainable, and adaptive AI systems.Researchers and students seeking a structured understanding of retrieval-based reasoning and reflection.< Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Seller: Greenworld Books, Arlington, TX, U.S.A.
Condition: very_good. Fast Free Shipping â" Very Good condition book with a firm cover and clean pages. Shows normal use and some light wear or limited notes markings. A solid, nice copy to enjoy.
Language: Sanskrit
Published by Chinmaya International Foundation
ISBN 10: 9380864159 ISBN 13: 9789380864150
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. pp. 107.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: Recycle Bookstore, San Jose, CA, U.S.A.
Paperback. Condition: Good. Book has mild wear to edges and noticable crease to front cover, light scratches to covers. Otherwise, solid working copy with clean, unmarked interior. Pictures available upon request.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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