LLM Engineer's Handbook
A framework for building LLM and RAG apps.
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.
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.
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.
What the Book is About?
What Makes it 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.
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.
This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world.
Julien Chaumond, CTO and CO-founder, Hugging Face
Get Your Copy
A special perk from Decoding AI Magazine. You can buy the book 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 from Packt, you can grab it on Amazon.
We can’t offer discounts there, but Amazon often has its own deals, sometimes up to 40% off!