Winning Big With Small AI

Winning Big With Small AI

Mark Watson
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Table of Contents

Winning Big With Small AI

  • Preface
    • Choosing Which “Small AI” Tools to Use in This Book
  • Part I - Advantages of Small AI With Examples
  • Advantages of Small Models with Examples That Might Surprise You
    • The Great Divergence: Redefining Intelligence in the Post-Scale Era
    • The Cloud Efficiency Champion: Anatomy of Gemini 3 Flash
    • The Local Insurgency: Qwen 3 and other Local Models Provide Democratization of Reasoning
    • The Physics of Thought: Theoretical Mechanisms
    • Sovereignty and Solvency: The Economic and Legal Case
    • Surprising Realities: Case Studies
    • Horizon 2026: The Future of Distributed Intelligence
  • Drawbacks of Small AI
    • Reduced Reasoning Depth and Nuance
    • Higher Propensity for Hallucination
    • Limited Context Window and Memory Management
    • Fragility in Prompt Following
    • Security and Support Disparities
    • Wrap Up for Drawbacks of Small AI
  • Part II - Defining Metrics for Success Using LLMs
  • Defining Metrics for Success Using LLMs
    • Operational Metrics: The Small AI Advantage
    • Quality Metrics: The “LLM-as-a-Judge” Pattern
    • RAG Metrics: The Trinity of Trust
    • Deterministic Guardrails
    • The Golden Ratio: Accuracy per Watt
    • Code Example: The Judge Pattern
    • Wrap Up for LLM Metrics
  • A Fair Shootout: Evaluating Large vs. Small Models for Task Fitness
    • The Setup for RAG: David vs. Goliath
    • Fairness: Adjusting Problem Setups for Small vs. Large Models
    • Comparing Small vs. Large Models for NLP and Extracting Structured Data From Text
    • Wrap Up for Evaluating Large vs. Small Models for Task Fitness
  • Part III - Python Examples for Switching Between Models
  • Using the LiteLLM Library to Easily Switch Between Models and Model Providers
    • Tool Use with LiteLLM
    • LiteLLM Wrap Up
  • Automatically Routing to the ‘Best’ Model Using RouteLLM Library
    • Implementation of a Command Line Query Utility Using RouteLLM
  • Wrap Up for Winning Big With Small AI
    • The Engineering Reality: From Vibes to Metrics
    • The Architecture of Hybrid Intelligence
    • The “Fitness for Purpose” Philosophy
    • Sovereignty, Solvency, and Sustainability
    • Final Recommendations for Your Journey
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Winning Big With Small AI

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Winning Big With Small AI11 chapters

Begin ›
  1. Preface

  2. Part I - Advantages of Small AI With Examples

  3. Advantages of Small Models with Examples That Might Surprise You

  4. Drawbacks of Small AI

  5. Part II - Defining Metrics for Success Using LLMs

  6. Defining Metrics for Success Using LLMs

  7. A Fair Shootout: Evaluating Large vs. Small Models for Task Fitness

  8. Part III - Python Examples for Switching Between Models

  9. Using the LiteLLM Library to Easily Switch Between Models and Model Providers

  10. Automatically Routing to the ‘Best’ Model Using RouteLLM Library

  11. Wrap Up for Winning Big With Small AI