Название: How Large Language Models Work Автор: Edward Raff, Drew Farris, Stella Biderman Издательство: Manning Publications Год: 2025 Страниц: 200 Язык: английский Формат: pdf (true) Размер: 18.2 MB
Learn how large language models like GPT and Gemini work under the hood in plain English.
How Large Language Models Work translates 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.
In How Large Language Models Work you will learn how to:
• Test and evaluate LLMs • Use human feedback, supervised fine-tuning, and Retrieval augmented generation (RAG) • Reducing the risk of bad outputs, high-stakes errors, and automation bias • Human-computer interaction systems • Combine LLMs with traditional ML
How Large Language Models Work is written by some of the best Machine Learning researchers at Booz Allen Hamilton, including researcher Stella Biderman, Director of AI/ML Research Drew Farris, and Director of Emerging AI Edward Raff. In clear and simple terms, these experts lay out the foundational concepts of LLMs, the technology’s opportunities and limitations, and best practices for incorporating AI into your organization.
About the book:
How Large Language Models Work is an introduction to LLMs that explores OpenAI’s GPT models. The book takes you inside ChatGPT, showing how a prompt becomes text output. In clear, plain language, this illuminating book shows you when and why LLMs make errors, and how you can account for inaccuracies in your AI solutions. Once you know how LLMs work, you’ll be ready to start exploring the bigger questions of AI, such as how LLMs “think” differently that humans, how to best design LLM-powered systems that work well with human operators, and what ethical, legal, and security issues can—and will—arise from AI automation.
About the reader: Includes examples in Python. No knowledge of ML or AI systems is required.
This book is intended for a variety of readers, including those who have just started working with LLMs, experienced software developers, and data scientists, as well as technical leadership, decision makers, and executives in the C-suite, who face the challenge of developing strategies for incorporating LLMs and Generative AI into their businesses. Our goal in writing this book was to create a work that is both accessible and compelling for a broad audience, presenting LLMs in a nontrivial manner.
Perhaps you’ve previously encountered machine learning, either as a student or hobbyist who took an introduction to machine learning course, but you lack a strong foundation in the field. Perhaps you’re someone who has used tools like OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, or Microsoft’s Copilot for work or play and are curious about how these tools generate their results. Regardless of your background or experience, we believe there’s something for you in this book.
Once you’re done, you’ll know: How LLMs process human language data and identify the tasks that may fail when using an LLM How data flows through an LLM, the role of transformers and attention, how they operate at a high level, why they are important, and how they relate to other machine learning algorithms How LLMs are trained on data, including the concepts of parameters, gradient descent, pretraining, and why model size is critical How to choose a deployment strategy for LLMs in your applications and business How to identify tasks and scenarios that LLMs can’t realistically solve The dangers and ethical concerns of using and building LLMs and where it is appropriate or inappropriate to use them
Навигация
Вход на сайт
Реклама
How Large Language Models Work 
