Quick Start Guide to Large Language Models, 2nd Edition, 2nd edition

Foreword xi
Preface xiii
Acknowledgments xix
About the Author xxi

Part I: Introduction to Large Language Models 1

Chapter 1: Overview of Large Language Models 3
What Are Large Language Models? 4
Popular Modern LLMs 7
Applications of LLMs 25
Summary 31

Chapter 2: Semantic Search with LLMs 33
Introduction 33
The Task 34
Solution Overview 36
The Components 37
Putting It All Together 53
The Cost of Closed-Source Components 57
Summary 58

Chapter 3: First Steps with Prompt Engineering 59
Introduction 59
Prompt Engineering 59
Working with Prompts Across Models 70
Summary 74

Chapter 4: The AI Ecosystem: Putting the Pieces Together 75
Introduction 75
The Ever-Shifting Performance of Closed-Source AI 76
AI Reasoning versus Thinking 77
Case Study 1: Retrieval Augmented Generation 79
Case Study 2: Automated AI Agents 87
Conclusion 93

Part II: Getting the Most Out of LLMs 95

Chapter 5: Optimizing LLMs with Customized Fine-Tuning 97
Introduction 97
Transfer Learning and Fine-Tuning: A Primer 99
A Look at the OpenAI Fine-Tuning API 102
Preparing Custom Examples with the OpenAI CLI 104
Setting Up the OpenAI CLI 108
Our First Fine-Tuned LLM 109
Summary 119

Chapter 6: Advanced Prompt Engineering 121
Introduction 121
Prompt Injection Attacks 121
Input/Output Validation 123
Batch Prompting 126
Prompt Chaining 128
Case Study: How Good at Math Is AI? 135
Summary 145

Chapter 7: Customizing Embeddings and Model Architectures 147
Introduction 147
Case Study: Building a Recommendation System 148
Summary 166

Chapter 8: AI Alignment: First Principles 167
Introduction 167
Aligned to Whom and to What End? 167
Alignment as a Bias Mitigator 173
The Pillars of Alignment 176
Constitutional AI: A Step Toward Self-Alignment 195
Conclusion 198

Part III: Advanced LLM Usage 199

Chapter 9: Moving Beyond Foundation Models 201
Introduction 201
Case Study: Visual Q/A 201
Case Study: Reinforcement Learning from Feedback 218
Summary 228

Chapter 10: Advanced Open-Source LLM Fine-Tuning 229
Introduction 229
Example: Anime Genre Multilabel Classification with BERT 230
Example: LaTeX Generation with GPT2 244
Sinan's Attempt at Wise Yet Engaging Responses: SAWYER 248
Summary 271

Chapter 11: Moving LLMs into Production 275
Introduction 275
Deploying Closed-Source LLMs to Production 275
Deploying Open-Source LLMs to Production 276
Summary 297

Chapter 12: Evaluating LLMs 299
Introduction 299
Evaluating Generative Tasks 300
Evaluating Understanding Tasks 317
Conclusion 328
Keep Going! 329

Part IV: Appendices 331

Appendix A: LLM FAQs 333
Appendix B: LLM Glossary 339
Appendix C: LLM Application Archetypes 345

Index 349