Mira S Devlin (17 results)
Language: English
Published by Independently published, 2025
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Published by Independently Published, 2025
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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 evolv…e 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.
Language: English
Published by Amazon Digital Services LLC - Kdp, 2025
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Language: English
Published by Independently Published, 2025
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Language: English
Published by Independently Published, 2025
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Language: English
Published by Independently Published, 2025
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Language: English
Published by Independently Published, 2025
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Language: English
Published by Independently published, 2025
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Published by Independently published, 2025
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Language: English
Published by Independently Published, 2025
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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 evolv…e 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 Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Language: English
Published by Independently Published, 2025
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Language: English
Published by Independently Published, 2025
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Language: English
Published by Independently published, 2025
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Language: English
Published by Independently Published, 2025
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Paperback. Condition: new. Paperback. Build AI systems that don't just respond-they collaborate, adapt, and evolve.Large Language Models changed the world.Multi-agent LLM ecosystems will redefine it.In Scaling LLM Agents, Mira S. Devlin takes you inside the next revolution of AI architecture-where networks of specialized LLM age…nts coordinate, reason, reflect, and self-optimize to accomplish what a single model could never do alone.This is not another "prompting" book.This is a systems-engineering blueprint for building scalable, resilient, and tool-driven AI ecosystems that behave more like distributed organizations than standalone chatbots.Drawing from modern AI orchestration frameworks, emerging research on distributed cognition, and real-world production patterns, this book shows you how to design agent clusters, shared memory fabrics, routing layers, evaluators, and tool hubs that dynamically adapt and continuously improve.What You'll LearnHow to structure LLM agents into clusters with clear roles, capabilities, and communication protocolsCoordinator and scheduler patterns for robust multi-agent task executionDecentralized routing fabrics for fast, scalable message passingShared vector memory systems for persistent state, grounding, and context fusionReflection and optimization loops that help agents correct themselves and learn from outcomesTool-driven orchestration using APIs, function calling, and external workflowsMonitoring, evaluation, and telemetry layers to keep your multi-agent system safe, reliable, and transparentScalable system topologies for production workloads, real-time reasoning, and enterprise automationWho This Book Is ForAI engineers designing intelligent, high-performance systemsSoftware architects modernizing applications with agentic patternsFounders and CTOs building AI-first productsResearchers exploring distributed cognition and emergent behaviorsDevelopers wanting to go beyond prompts and learn real LLM engineeringIf you've mastered prompting, tinkered with agents, or built early prototypes-this is the book that takes you into the next era: true multi-agent AI ecosystems that scale.Why This Book MattersThe future will not belong to the biggest model.but to the best-organized constellation of collaborating models.This book gives you the architecture, patterns, and practical frameworks to build them.Order your copy today and start building the AI ecosystems of tomorrow. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by Independently Published, 2025
- Softcover
- Print on Demand
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Paperback. Condition: new. Paperback. Build AI systems that don't just respond-they collaborate, adapt, and evolve.Large Language Models changed the world.Multi-agent LLM ecosystems will redefine it.In Scaling LLM Agents, Mira S. Devlin takes you inside the next revolution of AI architecture-where networks of specialized LLM age…nts coordinate, reason, reflect, and self-optimize to accomplish what a single model could never do alone.This is not another "prompting" book.This is a systems-engineering blueprint for building scalable, resilient, and tool-driven AI ecosystems that behave more like distributed organizations than standalone chatbots.Drawing from modern AI orchestration frameworks, emerging research on distributed cognition, and real-world production patterns, this book shows you how to design agent clusters, shared memory fabrics, routing layers, evaluators, and tool hubs that dynamically adapt and continuously improve.What You'll LearnHow to structure LLM agents into clusters with clear roles, capabilities, and communication protocolsCoordinator and scheduler patterns for robust multi-agent task executionDecentralized routing fabrics for fast, scalable message passingShared vector memory systems for persistent state, grounding, and context fusionReflection and optimization loops that help agents correct themselves and learn from outcomesTool-driven orchestration using APIs, function calling, and external workflowsMonitoring, evaluation, and telemetry layers to keep your multi-agent system safe, reliable, and transparentScalable system topologies for production workloads, real-time reasoning, and enterprise automationWho This Book Is ForAI engineers designing intelligent, high-performance systemsSoftware architects modernizing applications with agentic patternsFounders and CTOs building AI-first productsResearchers exploring distributed cognition and emergent behaviorsDevelopers wanting to go beyond prompts and learn real LLM engineeringIf you've mastered prompting, tinkered with agents, or built early prototypes-this is the book that takes you into the next era: true multi-agent AI ecosystems that scale.Why This Book MattersThe future will not belong to the biggest model.but to the best-organized constellation of collaborating models.This book gives you the architecture, patterns, and practical frameworks to build them.Order your copy today and start building the AI ecosystems of tomorrow. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
