How Large Language Models Work (True Retail EPUB)

Learn how large language models like GPT and Gemini work under the hood in plain English.
How Large Language Models Worktranslates years of expert research on Large Language Models into a readable, focused introduction to working with these amazing systems. It explains clearly how LLMs function, introduces the optimization techniques to fine-tune them, and shows how to create pipelines and processes to ensure your AI applications are efficient and error-free.
InHow Large Language Models Workyou will learn how to:
Test and evaluate LLMsUse human feedback, supervised fine-tuning, and Retrieval Augmented Generation (RAG)Reducing the risk of bad outputs, high-stakes errors, and automation biasHuman-computer interaction systemsCombine LLMs with traditional ML
How Large Language Models Workis authored by top machine learning researchers at Booz Allen Hamilton, including researcherStella Biderman, Director of AI/ML ResearchDrew Farris, and Director of Emerging AIEdward Raff. They lay out how LLM and GPT technology works in plain language that's accessible and engaging for all.
About the Technology
Large Language Models put the "I" in "AI." By connecting words, concepts, and patterns from billions of documents, LLMs are able to generate the human-like responses we've come to expect from tools like ChatGPT, Claude, and Deep-Seek. In this informative and entertaining book, the world's best machine learning researchers from Booz Allen Hamilton explore foundational concepts of LLMs, their opportunities and limitations, and the best practices for incorporating AI into your organizations and applications.
About the Book
How Large Language Models Worktakes you inside an LLM, showing step-by-step how a natural language prompt becomes a clear, readable text completion. Written in plain language, you'll learn how LLMs are created, why they make errors, and how you can design reliable AI solutions. Along the way, you'll learn how LLMs "think," how to design LLM-powered applications like agents and Q&A systems, and how to navigate the ethical, legal, and security issues.
What's InsideCustomize LLMs for specific applicationsReduce the risk of bad outputs and biasDispel myths about LLMsGo beyond language processing
About the Readers
No knowledge of ML or AI systems is required.
About the Author
Edward Raff,Drew FarrisandStella Bidermanare the Director of Emerging AI, Director of AI/ML Research, and machine learning researcher at Booz Allen Hamilton.
Table of Contents
1 Big picture: What are LLMs?
2 Tokenizers: How large language models see the world
3 Transformers: How inputs become outputs
4 How LLMs learn
5 How do we constrain the behavior of LLMs?
6 Beyond natural language processing
7 Misconceptions, limits, and eminent abilities of LLMs
8 Designing solutions with large language models
9 Ethics of building and using LLMs

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