Produktbild: Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment
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Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.11.2025

Verlag

Pearson Education Limited

Seitenzahl

320

Maße (L/B/H)

23,5/17,8/1,8 cm

Gewicht

560 g

Auflage

1

Sprache

Englisch

ISBN

978-0-13-548968-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.11.2025

Verlag

Pearson Education Limited

Seitenzahl

320

Maße (L/B/H)

23,5/17,8/1,8 cm

Gewicht

560 g

Auflage

1

Sprache

Englisch

ISBN

978-0-13-548968-0

Herstelleradresse


Email: info@bod.de

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  • Produktbild: Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment

  • Series Editor Foreword xi



    Preface xiii



    Acknowledgments xvii



    About the Author xix


    Part I: Getting Started with Foundations of AI, LLMs, and Experimentation 1


    Chapter 1: An Introduction to AI, LLMs, and Agents 3



    Introduction 3



    The Basics of Large Language Models 3



    The Family Tree of LLM Tasks 10



    Alignment 10



    Prompt Engineering 12



    Special LLM Features 17



    LLM Workflows 25



    AI Agents 25



    Conclusion 28


    Chapter 2: First Steps with LLM Workflows 31



    Introduction 31



    Case Study 1: Text-to-SQL Workflow 32



    Conclusion 57


    Chapter 3: AI Evaluation Plus Experimentation 59



    Introduction 59



    Evaluating and Experimenting with LLMs 59



    Case Study 1, Revisited: The Text-to-SQL Workflow 61



    Case Study 2: A "Simple" Summary Prompt 77



    Conclusion 83


    Part II: Moving the Needle with AI Agents, Workflows, and Multimodality 85


    Chapter 4: First Steps with AI Agents and Multi-Agent Workloads 87



    Introduction 87



    Case Study 3: From RAG to Agents 88



    When Should You Use Workflows Versus Agents? 104



    Case Study 4: A (Nearly) End-to-End SDR 105



    Evaluating Agents 118



    Conclusion 121


    Chapter 5: Enhancing Agents with Prompting, Workflows, and More Agents 123



    Introduction 123



    Case Study 5: Agents Complying with Policies Plus Synthetic Data Generation 124



    Building Our Policy Bot Agent 127



    Case Study 6: Deep Research Plus Content Generation Agentic Workflows 133



    Multi-Agent Architectures 141



    Case Study 4, Revisited: Adding a Supervisor Agent to Our SDR Team 148



    Case Study 7: Agentic Tool Selection Performance 149



    Conclusion 157


    Chapter 6: Moving Beyond Natural Language: Multimodal and Coding AI 159



    Introduction 159



    Introduction to Multimodal AI 159



    Case Study 8: Image Retrieval Pipelines 168



    Case Study 9: Visual Q/A with Moondream 174



    Case Study 10: Coding Agent with Image Generation, File Use, and Moondream 176



    The Case for Any-to-Any Models 188



    Conclusion 191


    Part III: Optimizing Workloads with Fine-Tuning, Frameworks, and Reasoning LLMs 193


    Chapter 7: Reasoning LLMs and Computer Use 195



    Introduction 195



    Seven Pillars of Intelligence 195



    Case Study 11: Benchmarking Reasoning Models 198



    Reasoning Models for ReAct Agents 210



    Case Study 12: Computer Use 212



    Conclusion 224


    Chapter 8: Fine-Tuning AI for Calibrated Performance 225



    Introduction 225



    Case Study 13: Classification Versus Multiple Choice 227



    Case Study 14: Domain Adaptation 245



    Conclusion 258


    Chapter 9: Optimizing AI Models for Production 261



    Introduction 261



    Model Compression 261



    Case Study 15: Speculative Decoding with Qwen 269



    Case Study 16: Voice Bot--Need for Speed 272



    Case Study 17: Fine-Tuning Matryoshka Embeddings 277



    Case Study
    N
    + 1: What Comes Next? 284


    Index 287