Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs

Description

The advancement of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing in recent years. Models like BERT, T5, and ChatGPT have demonstrated unprecedented performance on a wide range of NLP tasks, from text classification to machine translation. Despite their impressive performance, the use of LLMs remains challenging for many practitioners. The sheer size of these models, combined with the lack of understanding of their inner workings, has made it difficult for practitioners to effectively use and optimize these models for their specific needs. Title Page Contents at a Glance Table of Contents Preface Part I: Introduction to Large Language Models 1. Overview of Large Language Models What Are Large Language Models (LLMs)? Popular Modern LLMs Domain-Specific LLMs Applications of LLMs Summary 2. Launching an Application with Proprietary Models Introduction The Task Solution Overview The Components Putting It All Together The Cost of Closed-Source Summary 3. Prompt Engineering with GPT3 Introduction Prompt Engineering Working with Prompts Across Models Building a Q/A bot with ChatGPT Summary 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 Our First Fine-Tuned LLM! Case Study 2: Amazon Review Category Classification Summary Part II: Getting the most out of LLMs 5. Advanced Prompt Engineering Introduction Prompt Injection Attacks Input/Output Validation Batch Prompting Prompt Chaining Chain of Thought Prompting Re-visiting Few-shot Learning Testing and Iterative Prompt Development Conclusion 6. Customizing Embeddings and Model Architectures Introduction Case Study – Building a Recommendation System Conclusion 7. Moving Beyond Foundation Models Introduction Case Study—Visual Q/A Case Study—Reinforcement Learning from Feedback Conclusion 8. Fine-Tuning Open-Source LLMs [This content is currently in development.] 9. Deploying Custom LLMs to the Cloud [This content is currently in development.]