The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Table of Contents:
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