Quick Start Guide to Large Language Models: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AI, 2nd Edition

Table of contents

1. Cover Page
2. About This eBook
3. Title Page
4. Copyright Page
5. Contents
6. Foreword
7. Preface
   1. Audience and Prerequisites
   2. How to Approach This Book
   3. Overview
   4. Unique Features
   5. Summary
8. Acknowledgments
9. About the Author
10. Part I: Introduction to Large Language Models
    1. 1. Overview of Large Language Models
       1. What Are Large Language Models?
       2. Popular Modern LLMs
       3. Applications of LLMs
       4. Summary
    2. 2. Semantic Search with LLMs
       1. Introduction
       2. The Task
       3. Solution Overview
       4. The Components
       5. Putting It All Together
       6. The Cost of Closed-Source Components
       7. Summary
    3. 3. First Steps with Prompt Engineering
       1. Introduction
       2. Prompt Engineering
       3. Working with Prompts Across Models
       4. Summary
    4. 4. The AI Ecosystem: Putting the Pieces Together
       1. Introduction
       2. The Ever-Shifting Performance of Closed-Source AI
       3. AI Reasoning versus Thinking
       4. Case Study 1: Retrieval Augmented Generation
       5. Case Study 2: Automated AI Agents
       6. Conclusion
11. Part II: Getting the Most Out of LLMs
    1. 5. Optimizing LLMs with Customized Fine-Tuning
       1. Introduction
       2. Transfer Learning and Fine-Tuning: A Primer
       3. A Look at the OpenAI Fine-Tuning API
       4. Preparing Custom Examples with the OpenAI CLI
       5. Setting Up the OpenAI CLI
       6. Our First Fine-Tuned LLM
       7. Summary
    2. 6. Advanced Prompt Engineering
       1. Introduction
       2. Prompt Injection Attacks
       3. Input/Output Validation
       4. Batch Prompting
       5. Prompt Chaining
       6. Case Study: How Good at Math Is AI?
       7. Summary
    3. 7. Customizing Embeddings and Model Architectures
       1. Introduction
       2. Case Study: Building a Recommendation System
       3. Summary
    4. 8. AI Alignment: First Principles
       1. Introduction
       2. Aligned to Whom and to What End?
       3. Alignment as a Bias Mitigator
       4. The Pillars of Alignment
       5. Constitutional AI: A Step Toward Self-Alignment
       6. Conclusion
12. Part III: Advanced LLM Usage
    1. 9. Moving Beyond Foundation Models
       1. Introduction
       2. Case Study: Visual Q/A
       3. Case Study: Reinforcement Learning from Feedback
       4. Summary
    2. 10. Advanced Open-Source LLM Fine-Tuning
       1. Introduction
       2. Example: Anime Genre Multilabel Classification with BERT
       3. Example: LaTeX Generation with GPT2
       4. Sinan’s Attempt at Wise Yet Engaging Responses: SAWYER
       5. Summary
    3. 11. Moving LLMs into Production
       1. Introduction
       2. Deploying Closed-Source LLMs to Production
       3. Deploying Open-Source LLMs to Production
       4. Summary
    4. 12. Evaluating LLMs
       1. Introduction
       2. Evaluating Generative Tasks
       3. Evaluating Understanding Tasks
       4. Conclusion
       5. Keep Going!
13. Part IV: Appendices
    1. A. LLM FAQs
       1. The LLM already knows about the domain I’m working in. Why should I add any few-shot examples or grounded content as in RAG?
       2. I just want to deploy a closed-source API. What are the main things I need to look out for?
       3. I really want to deploy an open-source model. What are the main things I need to look out for?
       4. Creating and fine-tuning my own model architecture seems hard. What can I do to make it easier?
       5. I think my model is susceptible to prompt injections or going off task. How do I correct it?
       6. Why didn’t we talk about third-party LLM tools like LangChain?
       7. How do I deal with overfitting or underfitting in LLMs?
       8. How can I use LLMs for non-English languages? Are there any unique challenges?
       9. How can I implement real-time monitoring or logging to understand the performance of my deployed LLM better?
       10. What are some things we didn’t talk about in this book?
    2. B. LLM Glossary
       1. Transformer Architecture
       2. Attention Mechanism
       3. Large Language Model (LLM)
       4. Autoregressive Language Models
       5. Autoencoding Language Models
       6. Transfer Learning
       7. Prompt Engineering
       8. Alignment
       9. Reinforcement Learning from Human Feedback (RLHF)
       10. Reinforcement Learning from AI Feedback (RLAIF)
       11. Corpora
       12. Fine-Tuning
       13. Labeled Data
       14. Hyperparameters
       15. Learning Rate
       16. Batch Size
       17. Training Epochs
       18. Evaluation Metrics
       19. Incremental/Online Learning
       20. Overfitting
       21. Underfitting
       22. Knowledge Distillation
       23. Task-Specific Distillation
       24. Task-Agnostic Distillation
       25. Multimodal Models
       26. Alignment
    3. C. LLM Application Archetypes
       1. General Chatbots / Retrieval Augmented Generation (RAG)
       2. Agents
       3. Fine-Tuning a Closed-Source LLM
       4. Fine-Tuning an Open-Source LLM
       5. Fine-Tuning a Bi-encoder to Learn New Embeddings
       6. Fine-Tuning an LLM for Following Instructions Using Both LM Training and Reinforcement Learning from Human / AI Feedback (RLHF and RLAIF)
       7. Open-Book Question-Answering
       8. Visual Question-Answering
14. Index
15. Credits
16. Code Snippets