Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs (Addison-Wesley Data & Analytics Series)

Cover Page
About This eBook
Halftitle Page
Title Page
Copyright Page
Pearson’s Commitment to Diversity, Equity, and Inclusion
Contents
Foreword
Preface
	Audience and Prerequisites
	How to Approach This Book
	Overview
	Unique Features
	Summary
Acknowledgments
About the Author
I: Introduction to Large Language Models
	1. Overview of Large Language Models
		What Are Large Language Models?
		Popular Modern LLMs
		Domain-Specific LLMs
		Applications of LLMs
		Summary
	2. Semantic Search with LLMs
		Introduction
		The Task
		Solution Overview
		The Components
		Putting It All Together
		The Cost of Closed-Source Components
		Summary
	3. First Steps with Prompt Engineering
		Introduction
		Prompt Engineering
		Working with Prompts Across Models
		Building a Q/A Bot with ChatGPT
		Summary
II: Getting the Most Out of LLMs
	4. Optimizing LLMs with Customized Fine-Tuning
		Introduction
		Transfer Learning and Fine-Tuning: A Primer
		A Look at the OpenAI Fine-Tuning API
		Preparing Custom Examples with the OpenAI CLI
		Setting Up the OpenAI CLI
		Our First Fine-Tuned LLM
		Case Study: Amazon Review Category Classification
		Summary
	5. Advanced Prompt Engineering
		Introduction
		Prompt Injection Attacks
		Input/Output Validation
		Batch Prompting
		Prompt Chaining
		Chain-of-Thought Prompting
		Revisiting Few-Shot Learning
		Testing and Iterative Prompt Development
		Summary
	6. Customizing Embeddings and Model Architectures
		Introduction
		Case Study: Building a Recommendation System
		Summary
III: Advanced LLM Usage
	7. Moving Beyond Foundation Models
		Introduction
		Case Study: Visual Q/A
		Case Study: Reinforcement Learning from Feedback
		Summary
	8. Advanced Open-Source LLM Fine-Tuning
		Introduction
		Example: Anime Genre Multilabel Classification with BERT
		Example: LaTeX Generation with GPT2
		Sinan’s Attempt at Wise Yet Engaging Responses: SAWYER
		The Ever-Changing World of Fine-Tuning
		Summary
	9. Moving LLMs into Production
		Introduction
		Deploying Closed-Source LLMs to Production
		Deploying Open-Source LLMs to Production
		Summary
IV: Appendices
	A. LLM FAQs
		The LLM already knows about the domain I’m working in. Why should I add any grounding?
		I just want to deploy a closed-source API. What are the main things I need to look out for?
		I really want to deploy an open-source model. What are the main things I need to look out for?
		Creating and fine-tuning my own model architecture seems hard. What can I do to make it easier?
		I think my model is susceptible to prompt injections or going off task. How do I correct it?
		Why didn’t we talk about third-party LLM tools like LangChain?
		How do I deal with overfitting or underfitting in LLMs?
		How can I use LLMs for non-English languages? Are there any unique challenges?
		How can I implement real-time monitoring or logging to understand the performance of my deployed LLM better?
		What are some things we didn’t talk about in this book?
	B. LLM Glossary
		Transformer Architecture
		Attention Mechanism
		Large Language Model (LLM)
		Autoregressive Language Models
		Autoencoding Language Models
		Transfer Learning
		Prompt Engineering
		Alignment
		Reinforcement Learning from Human Feedback (RLHF)
		Reinforcement Learning from AI Feedback (RLAIF)
		Corpora
		Fine-Tuning
		Labeled Data
		Hyperparameters
		Learning Rate
		Batch Size
		Training Epochs
		Evaluation Metrics
		Incremental/Online Learning
		Overfitting
		Underfitting
	C. LLM Application Archetypes
		Chatbots/Virtual Assistants
		Fine-Tuning a Closed-Source LLM
		Fine-Tuning an Open-Source LLM
		Fine-Tuning a Bi-encoder to Learn New Embeddings
		Fine-Tuning an LLM for Following Instructions Using Both LM Training and Reinforcement Learning from Human / AI Feedback (RLHF & RLAIF)
		Open-Book Question-Answering
Index
Permissions and Image Credits
Code Snippets