GPT AS GPT Generative Pre-trained Transformer as General-Purpose Technology

Readers will not only encounter a wide array of leading‑edge technologies; they will also glimpse their future trajectories. This volume is not a just specialist monograph but a work of general education and a practical guide for readers across many fields. I sincerely hope that many people will pick up this book and successfully build good relationships with this new kind of partner.

Prof. Koutaro Hachiya, Teikyo Heisei University, Japan

CONTENTS

FOREWORD.....................9

MOTIVATION FOR GPT AS GPT ...........10

A Definition of General-Purpose Technology and Its Relevance to AI.........10

Historical and Economic Context of GPTs ...........11

Overview of Foundational Models, LLMs, and Generative AI.........13

Guiding the Future of AI...............14

Previous works and results of the author used in the monograph .......15

1. AI AS GENERAL-PURPOSE TECHNOLOGY – BASIC CONCEPTS AND DEFINITIONS ............26

1.1. Technology Background of LLMs and Foundational Models .........26

1.2. Large language models......................36

1.3. A Brief History of the Development of LLM............37

1.4. Key Generative AI Models............39

1.5. What are Prompts?..........................45

1.6. Characteristics of an Effective Prompt..................46

1.7. Terms Used in Prompt Engineering................46

1.8. Use Cases of Prompt Engineering...............50

1.9. Chapter 1: Additional Readings ...........51

2. CREATING EFFECTIVE PROMPTS...............52

2.1. Problem Formulation and Prompt Engineering.........53

2.2. Improving Problem Formulation...............53

2.3. General Tips and Best Practices for Prompt Engineering..........54

2.4. Elements of a Prompt...............59

2.5. Examples ...........61

2.6. Chapter 2: Additional Readings ..........63

3. LANGUAGE MODEL SETTINGS AND ADAPTATION.............64

3.1. Downstream tasks ...........64

3.2. Fundamentals of LLM Adaptation ...........65

3.3. Examples ............72

3.4. Models Parametrization ..............73

3.5. Chapter 3: Additional Readings ...........78

4. REASONING AND CONTEXTUAL UNDERSTANDING IN PROMPT ENGINEERING......79

4.1. Historical Context of Reasoning in AI...........79

4.2. Evolution of Contextual Understanding in Language Models...........80

4.3. How LLMs Process and Generate Contextually Relevant Responses............81

4.4. The Importance of Context in Prompt Engineering........82

4.5. Strategies for Effectively Incorporating Context Indications.............83

4.6. Guiding Models to Reason and Make Inferences Based on Context..........84

4.7. Chapter 4: Additional Readings ...........85

5. ADVANCED PROMPT ENGINEERING TECHNIQUES AND APPLICATIONS......86

5.1. Zero-Shot .............88

5.2. Few-Shot ..........88

5.3. Chain-of-Thought (CoT) ..........89

5.4. Automatic Chain-of-Thought (Auto-CoT) ...........91

5.5. Self-Consistency............92

5.6. Logical Chain-of-Thought (LogiCoT) ................93

5.7. Chain-of-Symbol (CoS) ..........95

5.8. Tree-of-Thoughts (ToT) ...........96

5.9. Graph-of-Thoughts (GoT) ...............98

5.10. System 2 Attention (S2A) .............100

5.11. Thread of Thought (ThoT) .......101

5.12. Chain-of-Table ..............102

5.13. Retrieval Augmented Generation (RAG)............103

5.14. ReAct ...........105

5.15. Chain-of-Verification (CoVe) .........106

5.16. Chain-of-Note (CoN) ........108

5.17. Chain-of-Knowledge (CoK) ...........109

5.18. Active Prompting.............111

5.19. Automatic Prompt Engineer (APE).............112

5.20. Automatic Reasoning and Tool-use (ART).........114

5.21. Contrastive Chain-of-Thought (CCoT) .................115

5.22. Emotion Prompting.........116

5.23. Scratchpad Prompting.............117

5.24. Program of Thoughts (PoT) .........118

5.25. Structured Chain-of-Thought (SCoT) ........120

5.26. Chain-of-Code (CoC) .........121

5.27. Optimization by Prompting (OPRO)..........123

5.28. Rephrase and Respond (RaR) .........125

5.29. Take a Step Back Prompting........126

5.30. Verification Methods ...............127

5.31 Chapter 5: Additional Readings ...............134

6. APPLICATIONS OF PROMPT ENGINEERING IN CODE GENERATION.............135

6.1. Prompt Engineering in Software Development.......................135

6.2. Practical Applications of Prompt Engineering in Code Generation............138

6.3. Chapter 6: Additional Readings .........144

7. APPLICATIONS OF PROMPT ENGINEERING IN DATA AUGMENTATION........145

7.1. Data Augmentation.......145

7.2. The data augmentation process..........147

7.3. Techniques of Data Augmentation........150

7.4. Chapter 7: Additional Readings ........153

8. XAI...............154

8.1. XAI Significance.............154

8.2. History of XAI.........155

8.3. The main goals of XAI ..........155

8.4. Techniques in XAI.........156

8.5. Metrics for XAI...........168

8.6. Benefits of XAI..........170

8.7. Challenges in XAI..............171

8.8. Applications of XAI........172

8.9. Case Studies of XAI Implementation.........173

8.10. Challenges and Future Directions......174

8.11. Chapter 8: Additional Readings ........175

9. ETHICAL CONSIDERATIONS ........177

9.1. Ethical Implications in Prompt Engineering......177

9.2. Mitigating Undesired Consequences.....178

9.3. Principles of Responsible AI........179

9.4. Best Practices for Responsible AI.........179

9.5. Chapter 9: Additional Readings .......181

FIGURES.............182

EQUATIONS.......182

TABLES.......183

REFERENCES ............184