The AI Engineer's toolkit

Our first book: Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG

The AI Engineer's toolkit

I am super proud to share about a very special project we've been working on at Towards AI for the past 1.5 years along a dozen graduates and experts in the field... our new and first book: Building LLMs for Production!

One of the reasons I quit my PhD in AI was to build practical solutions that will help others in the real world and improve what exists. While I love the world of academia, when I first stepped into the startup world, it felt like I pretty much knew nothing all over again. I needed to understand the problems in the real-world application of AI and build solutions for it. Not just research but real models, real products, and real people using them. But here's the thing: grasping these challenges is merely the first step. For the ‘how’ part of it, you need to get into the code, the architecture, the models, the APIs, the deployments, the trials and errors, and the complex and wide varieties of frameworks - you don't have time to reinvent the wheel in a startup! So we've gathered everything we worked on and with in this 470-page book all about LLMs and how to work with them. Right now, this means working with LlamaIndex, LangChain, Activeloop and other of such amazing tools, but we believe the book still teaches concepts that will stay relevant for a long time even as LLMs get better, such as reducing hallucinations, teaching how to work and use them, some cool theory and tips and more.

Of course, I made a video giving more details about the book if you are curious:

p.s. The only skill required for the book is some Python (or programming) knowledge.

"Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG" is now available on Amazon! We are also on Goodreads with the same name. If you want to support us for free, give a review there.

Get the book now and let us know what you think:

Here are a few testimonials we've got from our early reviewers to give you a better idea of what this book is about:

"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."

“This book will help you or your company get the most out of LLMs. This book was an incredible guide of how to leverage cutting edge AI models and libraries to build robust tools that minimize the pitfalls of the current technology. It takes you from theory all the way to application with excellent, hands-on examples along the way. It is a must read for anyone looking to build a LLM product. “
  • Ken Jee, Head of Data Science and Podcast host (Ken's Nearest Neighbors, Exponential Athlete)

"For whoever interested in getting started with LLMs and all that comes with it, this is the book for you. It starts from explaining what an LLM is in simpler terms, and takes you through a brief history of time in NLP to the most current state of technology in AI. The broad range of topics covered with easy to understand examples will help any readers, and developers be in the know of the theory behind LLMs, prompt engineering, RAG, orchestration platforms and more. I highly recommend this book."
  • Sonam Gupta, PhD, Developer Advocate, Experienced Data Scientist, PhD Data Science and Podcast host

“This textbook not only explores the critical aspects of LLMs, including their history and evolution, but it also equips AI Engineers of the Future with the tools and techniques that will set them apart from their peers. You will enjoy diving into challenging and important subjects such as Prompt Engineering, Agentic AI, SFT, RLHF, Quantization, and more, with the support of code, to best understand what it takes to develop, fine tune, and deploy LLMs in diverse environments.”
  • Greg Coquillo, AI Product Leader and LinkedIn Top Voice

"This book is the most thorough overview of LLMs I've come across. An excellent primer for newcomers and a valuable reference for experienced practitioners."
  • Shaw Talebi, Founder of The Data Entrepreneurs, AI Educator and Advisor

"A must-read for development of customer-facing LLM applications. The defacto manual for AI Engineering. This book provides practical insights and real-world applications of, inter alia, RAG systems and prompt engineering. Seriously, pick it up."
  • Ahmed Moubtahij, ing., NLP Scientist/ML Engineer

"Books quickly get out of date in the ever evolving AI field. So, not often one can get their hands on a book offering the latest insights into Large Language Models (LLMs). This book is a comprehensive guide (with code!) covering all important things: from architecture basics, to prompting, finetuning, retrieval augmentation, building agents, and LLM deployment."
  • Letitia Parcalabescu, NLP PhD Candidate and YouTuber

“A comprehensive and well-rounded resource that covers all the fundamentals of LLMs with a well-struck balance between theory and code. AI is a rapidly developing field, and there are many resources released every day. However, this is a book I will come back to again and again, regardless of how the field of AI evolves. Even though I usually fall asleep when reading books but this one has code in it and examples that can ELI5, and it’s very important, so that is motivating”
  • Tina Huang, Founder of Lonely Octopus, YouTuber, Ex-Meta