Building LLMs for Production Gets a Massive Update!

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

We are excited to announce the new and improved version of Building LLMs for Production. The latest version of the book offers an improved structure, fresher insights, more up-to-date information, and optimized code, along with an overall smoother, more enjoyable reading experience.

While the book is grounded in ‘timeless principles’ that remain relevant despite ongoing developments in the LLM field, this update aims to make the reading experience smoother and more accessible, ensuring that key concepts are easier to understand.

The core concepts discussed in the book are becoming a foundation for practitioners and companies working with LLMs. The updated version provides more practical information on these techniques, which we believe have become more accessible since the book was published and have found broader applications beyond research.

The updated version is available as a paperback, e-book, & hardcover on your local Amazon. Check it out here!

What’s New?

This update emphasizes structure, accessibility, and practical utility. By improving the flow of the book to follow a more natural “beginner to advanced” trajectory, the updated version allows readers to build their understanding progressively, making even complex concepts easier to grasp. Key highlights include:

New Chapter on Indexing and Data Preparation

A major addition to the book is a brand-new chapter titled Indexes, Retrievers, and Data Preparation. Indexes, Retrievers, and Data Preparation are the foundational components of a RAG pipeline. The latest version puts more emphasis on these to ensure the RAG pipelines taught in the book help in scaling LLM applications, optimizing performance, and enhancing response quality. This chapter introduces crucial concepts around creating indexes, retrieving data, and chunking large datasets into more manageable pieces.

Additionally, several updates have been made to the existing chapters to include more examples and a better understanding of real-world applications.

New Concepts and Techniques

The updated version takes a deeper dive into essential techniques for LLM deployment and optimization, making it more practical and relevant for current AI development needs. For example, the book explores model distillation, a powerful technique to reduce inference costs and improve latency, with a detailed case study on Google’s Gemma 2, demonstrating its real-world impact.

With open-source LLMs growing in popularity, this edition also covers the deployment of LLMs on various cloud platforms, including Together AI, Groq, Fireworks AI, and Replicate. This broader approach helps readers find cost-effective and scalable solutions for real-world applications.

Additionally, while we recommend frameworks like LlamaIndex and LangChain for building Retrieval-Augmented Generation (RAG) pipelines, this update also includes a comprehensive, step-by-step tutorial on building a basic RAG pipeline from scratch. This foundation equips you to apply modern frameworks like LlamaIndex and LangChain more efficiently or go on your own with custom implementations.

Staying True to the Foundations

While these updates reflect the latest advancements in the field, the book remains grounded in the core principles that made it a valuable resource to begin with. Concepts like prompt engineering, fine-tuning, and RAG are still at the heart of the book, with practical examples to guide readers in adapting LLMs to specific use cases. The balance between theory and applied knowledge ensures that readers gain both an understanding of foundational AI principles and the skills to deploy these technologies effectively.

Get a sneak peek into more updates on Amazon!

For First-Time Readers

For those new to Building LLMs for Production, the book serves as a complete AI engineering toolkit for building LLM applications. Curated by a team of experts from Towards AI, Activeloop, LlamaIndex, Mila, and more, it offers an end-to-end roadmap to mastering the key techniques and tools needed to leverage LLMs in production environments.

The book focuses on adapting LLMs to specific use cases by leveraging Prompt Engineering, Fine-Tuning, and RAG. It is tailored for readers with an intermediate knowledge of Python, although no programming knowledge is necessary to explore this book’s AI and LLM-specific concept explanations.

Additionally, the book is packed with theories, concepts, projects, applications, and experience that you can confidently put on your CVs.

The up-to-date version is available as a paperback, e-book, and hardcover on Amazon!

What Industry Experts Are Saying

Building LLMs for Production has earned praise from leading figures in AI:

• Jerry Liu, Co-founder and CEO of LlamaIndex: “This is the most comprehensive textbook to date on building LLM applications — all essential topics in an AI Engineer’s toolkit.”
• Shashank Kalanithi, Data Engineer at Meta: “An indispensable guide for anyone venturing into the world of large language models. It’s a must-have in the library of every aspiring and seasoned AI professional.”
• Pete Huang, Co-founder of The Neuron: “A truly wonderful resource that develops understanding of LLMs from the ground up, from theory to code and modern frameworks.”
• Jeremy Pinto, Senior Applied Research Scientist at Mila: “This book covers everything you need to know to start applying LLMs in a pragmatic way — it balances the right amount of theory and applied knowledge, providing intuitions, use-cases, and code snippets.”

Whether you’re just getting started with LLMs or you’re already working in the AI field, the updated Building LLMs for Production offers an invaluable resource. From improved content to practical examples and the latest frameworks, this edition provides you with all the essentials to navigate the evolving landscape of large language models.

Check out the latest version today on Amazon!

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Published via Towards AI