LLM Engineer's Handbook is now thriving!

A framework for building LLM and RAG apps

This is not just another article but a celebration of a remarkable milestone and an incredible journey.

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Over 10,000 Copies Sold – Thank You!

We’re excited to announce that the LLM Engineer's Handbook has reached over 10,000 readers worldwide!

A heartfelt thank you to everyone who has read, shared, or supported the book. Your belief in this project means everything to us as we shape the future of GenAI development together.

If you haven’t picked up the book yet, it’s never too late to join the journey. Let’s build the future of AI together!

The Journey of Writing the Book

Reflecting on our journey, I’m proud of what Maxime and I accomplished.

In the summer of 2024, we embarked on this ambitious project, with me leaving my job to focus fully on it while Maxime balanced everything like a pro. Despite sleepless nights and tight deadlines, we produced a book emphasizing quality and originality.

My open-source LLM Twin course was a primary source of inspiration for this book. I am fascinated by the idea of engineering your LLM Twin (a digital version of yourself—I am a massive fan of Black Mirror), a process that requires deep knowledge of AI engineering, from RAG to fine-tuning.

By writing this book with Maxime and using our combined skills, we had the opportunity to explore the LLM Twin topic in depth.

On a personal note, I predict we will soon be able to start chatting with the digital version of ourselves, and I want to be part of that revolution.

What the Book is About

The lack of standardization can make building scalable, robust and accurate LLM solutions a real challenge. As an emerging field, you have a plethora of algorithms, tools, and design principles to choose from, which can be highly confusing and daunting.

Thus, this book aims to provide a set of principles and a framework for structuring your thinking about what’s required to build an end-to-end LLM system while being flexible enough to adapt it to your needs when working with GenAI.

Even if algorithms evolve, this book is valuable for understanding the steps required to build production-ready LLM applications.

During the book, we will build a production-ready MVP, an LLM Twin (your digital AI replica), as you can see in the book’s open-source GitHub repository.

However, the framework presented can easily be adapted to your use case (the book's end goal). If you use it to develop your ideas further, we would love to share what you’ve built on Substack or LinkedIn!

To find out more, visit our book page on Amazon.

What Makes the Book Unique

As LLM systems are not yet standardized, designing the architecture of the LLM Twin application was a fantastic journey. I had to understand how to adapt standard MLOps and ML system design principles to implement LLM, RAG and LLMOps solutions.

Thus, this book's emphasis on practicality sets it apart. It provides a framework for architecting and building LLM apps that can be adapted to your needs.

While walking you through the framework, we present the complete lifecycle of an LLM app, connecting the dots between DE, SWE, GenAI, and MLOps while building the LLM Twin MVP.

This book goes beyond coding; it presents a mind map for architecting future ideas.

Get Your Copy

If you’d like to support our work, consider purchasing the book.

As a special perk for the Decoding ML readers, you can buy it from Packt’s site with the following discounts:

• 20% off using affiliate code EDecodeML20 (eBook)

• 10% off using affiliate code PDecodeML10 (Print)

If you can’t order it from Packt, you can still order it from Amazon (where, unfortunately, we can’t offer any discounts as we play by Amazon’s rules):