Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG.
With all future updates · 10,000+ copies sold · 84 lessons, taught as a course
Please note: this e-book is an interactive resource, not a downloadable PDF.
“This is the most comprehensive textbook to date on building LLM applications, and helps learners understand everything from fundamentals to the simple-to-advanced building blocks of constructing LLM applications. The application topics include prompting, RAG, agents, fine-tuning, and deployment - all essential topics in an AI Engineer's toolkit.”
“A truly wonderful resource that develops understanding of LLMs from the ground up, from theory to code and modern frameworks. Grounds your knowledge in research trends and frameworks that develop your intuition around what's coming. Highly recommend.”
“An indispensable guide for anyone venturing into the world of large language models…Covering everything from theory to practical deployment, it’s a must-have in the library of every aspiring and seasoned AI professional.”
“It contains thorough explanations and code for you to start using and deploying LLMs, as well as optimizing their performance. Very highly recommended!”
“It covers the foundational aspects of LLMs as well as advanced use-cases like finetuning LLMs, Retrieval Augmented Generation and Agents. This will be valuable to anyone looking to dive into the field quickly and efficiently.”
The concepts are clearly explained, and the sample code really helps reinforce the material. I now need to develop a project and put this all together.
Mark ChaseIf you have aspirations to dive into the world of generative artificial intelligence (GenAI) and large language models (LLMs), you could definitely do worse than starting with this book. As the title implies, it is focused on building and thus relatively light on theory. It teaches you what you need to know behind the scenes but not much more than that. For instance, there is almost no math. The tutorials and code samples are the highlight of the book, as they exemplify how the literature is actually put into practice. As with any field, the more knowledge you already possess coming into this book, the less value you will find in reading it. However, everyone should find something worthwhile. One area of this book I feel could really be improved upon is the section on deployment. Running a GenAI app locally on your laptop is a very different game from running it in production in terms of scalability. An app that runs smoothly for ten users will incur previously unseen issues when deployed for ten thousand and will incur even more issues for ten million. Scaling, debugging and troubleshooting in production deserve more attention than is given in this book for it to maximize its value for professional AI engineers. Overall, it is well worth a thorough reading and should prove to be of aid to your career if you wish to step into this field.
Frederick Zhang"Building LLMs for Production" is an invaluable guide for anyone looking to deploy large language models efficiently and effectively. What sets this book apart is its all-in-one approach, covering everything from model architecture and optimization to scaling and deployment—all in a clear, accessible format that both beginners and experts can appreciate. The authors take a truly user-centric perspective, ensuring that practical implementation remains front and center. Whether you're integrating LLMs into existing workflows or building from scratch, this book simplifies complex concepts while maintaining technical depth. For developers and AI practitioners looking for a comprehensive, no-fluff resource, "Building LLMs for Production" is the go-to playbook for modern AI deployment. Highly recommended!"
Brian LangrinI am super excited to recommend this book to everyone. Written in a very excellent manner and covering all the essential details and concepts in the world of large language models. The complex and difficult concepts are easily graspable and the text is fully focused and coherent. Highly recommended book for academia as well as industry people.
Jamshaid SohailI recently read Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG, and I couldn’t be more satisfied with the insights and practical knowledge it provided. As someone involved in building robust AI-driven solutions, I found this book incredibly useful. It breaks down complex concepts like prompting strategies, fine-tuning techniques, and Retrieval-Augmented Generation (RAG) into manageable, actionable steps. The explanations are clear, and the examples are practical and relevant to real-world applications. This book is a must-have for anyone looking to take their LLMs from experimental stages to reliable, production-ready tools. Highly recommended!
James OdendalIf you are interested in building AI apps, this book serves as a fantastic icebreaker, being one of the few within the AI space worth your time and money.
Paul Iusztinnice balance between intuitive explanations and code
Roberto PardoThis is an excellent book and I would like to recommend this book to everyone. I was having little idea about how to build an application and plan for production deployment but after going through this book , I came across the various techniques to take into account to develop application and its deployment aspects. This book is worth every penny.
Vinay GuptaThe second version of this book can easily be considered a must-read as well as the first version. It's great to have key and evolving concepts explained like this!
Eugenio GaliotoThe GO-TO guy about AI and LLM
Hiroto MatsushimaCovers every aspect of a broad range of topics.
soumen nayakThe book has great coverage of nearly all the important topics related to LLMs and application-building with LLMs. I also liked the focus on the hands-on projects, so that you are not just reading but also trying things out.
Priyankar KumarTo build scalable and reliable products with LLMs
Get Your Copy Now!
This book explores various methods to adapt "foundational" LLMs to specific tasks with enhanced accuracy, reliability, and scalability. It tackles the lack of reliability of "out of the box" LLMs by teaching the AI developer tech stack of the future; Prompting, Fine-Tuning, RAG, and Tools Use.
The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It breaks down techniques that are scalable for enterprise-level workflows, helping both independent developers and small companies with limited resources create AI products that deliver value to paying customers.
The book is for anyone who wants to build LLM products that can serve real use cases today. It comes with access to our webpage where we also share lots of additional up-to-date content, code, notebooks, and resources. However, the coding parts of the book is tailored for readers with an intermediate knowledge of Python.