{"product_id":"how-large-language-models-work-9781633437081","title":"How Large Language Models Work","description":"\u003cb\u003eLearn how large language models like GPT and Gemini work under the hood in plain English.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e\u003ci\u003eHow Large Language Models Work\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eHow Large Language Models Work\u003c\/i\u003e you will learn how to: \u003cp\u003e\u003c\/p\u003e - Test and evaluate LLMs\u003cbr\u003e - Use human feedback, supervised fine-tuning, and Retrieval Augmented Generation (RAG)\u003cbr\u003e - Reducing the risk of bad outputs, high-stakes errors, and automation bias\u003cbr\u003e - Human-computer interaction systems\u003cbr\u003e - Combine LLMs with traditional ML \u003cp\u003e\u003c\/p\u003e\u003ci\u003eHow Large Language Models Work\u003c\/i\u003e is authored by top machine learning researchers at Booz Allen Hamilton, including researcher \u003cb\u003eStella Biderman\u003c\/b\u003e, Director of AI\/ML Research \u003cb\u003eDrew Farris\u003c\/b\u003e, and Director of Emerging AI \u003cb\u003eEdward Raff\u003c\/b\u003e. They lay out how LLM and GPT technology works in plain language that's accessible and engaging for all. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the Technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 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. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the Book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eHow Large Language Models Work\u003c\/i\u003e takes 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\u0026amp;A systems, and how to navigate the ethical, legal, and security issues. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhat's Inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e - Customize LLMs for specific applications\u003cbr\u003e - Reduce the risk of bad outputs and bias\u003cbr\u003e - Dispel myths about LLMs\u003cbr\u003e - Go beyond language processing \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the Readers\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e No knowledge of ML or AI systems is required. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eEdward Raff\u003c\/b\u003e, \u003cb\u003eDrew Farris\u003c\/b\u003e and \u003cb\u003eStella Biderman\u003c\/b\u003e are the Director of Emerging AI, Director of AI\/ML Research, and machine learning researcher at Booz Allen Hamilton. \u003cp\u003e\u003c\/p\u003e Table of Contents \u003cp\u003e\u003c\/p\u003e 1 Big picture: What are LLMs?\u003cbr\u003e 2 Tokenizers: How large language models see the world\u003cbr\u003e 3 Transformers: How inputs become outputs\u003cbr\u003e 4 How LLMs learn\u003cbr\u003e 5 How do we constrain the behavior of LLMs?\u003cbr\u003e 6 Beyond natural language processing\u003cbr\u003e 7 Misconceptions, limits, and eminent abilities of LLMs\u003cbr\u003e 8 Designing solutions with large language models\u003cbr\u003e 9 Ethics of building and using LLMs \u003cp\u003e\u003c\/p\u003eGet a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eEdward Raff\u003c\/b\u003e is a Director of Emerging AI at Booz Allen Hamilton, where he leads the machine learning research team. He has worked in healthcare, natural language processing, computer vision, and cyber security, among fundamental AI\/ML research. The author of \u003ci\u003eInside Deep Learning\u003c\/i\u003e, Dr. Raff has over 100 published research articles at the top artificial intelligence conferences. He is the author of the Java Statistical Analysis Tool library, a Senior Member of the Association for the Advancement of Artificial Intelligence, and twice chaired the Conference on Applied Machine Learning and Information Technology and the AI for Cyber Security workshop. Dr. Raff's work has been deployed and used by anti-virus companies all over the world. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eDrew Farris\u003c\/b\u003e is a professional software developer and technology consultant whose interests focus on large scale analytics, distributed computing and machine learning. Previously, he worked at TextWise where he implemented a wide variety of text exploration, management and retrieval applications combining natural language processing, classification and visualization techniques. He has contributed to a number of open source projects including Apache Mahout, Lucene and Solr, and holds a master's degree in Information Resource Management from Syracuse University's iSchool and a B.F.A in Computer Graphics. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eStella Biderman\u003c\/b\u003e is a machine learning researcher at Booz Allen Hamilton and the executive director of the non-profit research center EleutherAI. She is a leading advocate for open source artificial intelligence and has trained many of the world's most powerful open source artificial intelligence algorithms. She has a master's degree in computer science from the Georgia Institute of Technology and degrees in Mathematics and Philosophy from the University of Chicago.\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":51552741556498,"sku":"9781633437081","price":45.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_7fc6736d-6e2b-4ddf-8349-2b0eb69e32d4.jpg?v=1754901924","url":"https:\/\/surprise-castle.myshopify.com\/products\/how-large-language-models-work-9781633437081","provider":"Surprise Castle","version":"1.0","type":"link"}