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

by Louis-François Bouchard, Louie Peters

Genres: Artificial Intelligence, Technology, Technical, Programming, Nonfiction, Engineering, Software

679 pages, Kindle Edition

Published May 5, 2024

About the author

Louis-François Bouchard is from Montréal, Canada, and is known as What's AI on YouTube.

He focuses on making AI accessible by sharing and explaining it in simple terms, sharing the new research state and applications for everyone, demystifying the AI “black box” for everyone, and sensitizing people about the risks of using it.

Louis-François recently dropped out of his Ph.D. at Mila/Polytechnique Montréal to focus on his love of education on YouTube and as a co-founder and CTO at Towards AI. He aims to build an industry-relevant skillset for working with AI and popularizing the field.

Reviews

[Louis-François Bouchard · Rating 5 out of 5]

Amazing book, and totally biased review :D

[Viktor Ciroski · Rating 3 out of 5]

Overall, the book serves as a strong introduction to building LLM pipelines, offering valuable insights into foundational concepts. It provides clear explanations of topics such as quantization and evaluation metrics, which are particularly useful for readers seeking to understand model optimization and performance assessment.

However, the book falls short in its coverage of production level deployment. While it effectively addresses research-oriented topics, it lacks sufficient depth on practical aspects such as model hosting, serving infrastructure, and deployment strategies areas that are critical for taking LLM applications into real world production environments.

In many respects, the book reads more like an applied research-oriented text than a practical engineering guide. Despite this, I continue to reference it regularly, particularly for its discussion on generation speed measured in tokens per minute as a useful proxy for understanding model latency. I would have appreciated a deeper exploration of similarly actionable metrics and implementation details tailored for production use cases.

[Prasanth Kancharla · Rating 4 out of 5]

In the rapidly evolving landscape of Generative AI, Building LLMs for Production offers a comprehensive foundation for creating production-grade LLM applications.

I especially liked the book's well-structured organization. It starts with introductory material that gradually builds up to cover the current generation of LLMs, prompt engineering, retrieval-augmented generation (RAGs), and fine-tuning. This logical progression makes it accessible and easy to follow.

Given the sheer volume of new material emerging daily, staying updated on developments in the field is overwhelming. This book highlights references to pivotal papers and advancements, providing invaluable context for understanding key concepts.

The supplementary material further enhances the learning experience. Thoughtfully crafted, it reinforces the book's content and serves as an excellent starting point for putting the discussed concepts into practice.

[Muhammad Nabeel · Rating 5 out of 5]

I highly recommend Building LLMs for Production by Louis-Francois Bouchard and Louie Peters for anyone looking to dive into the world of large language models. The book not only provides a solid theoretical foundation but also offers practical guidance on implementing, fine-tuning, and deploying LLMs effectively. With clear explanations, real-world examples, and coverage of essential frameworks like LangChain and LlamaIndex, it’s perfect for both beginners and experienced practitioners aiming to build reliable, production-grade AI systems. This is a must-read resource for understanding and leveraging the full potential of LLMs for anyone who's interested in either starting his freelancing career or just wanting to dive in to these things.

[Josua Naiborhu · Rating 5 out of 5]

top-notch book on covering the how llm works from the ground up. i like how the author uses simple analogy on laying out every concepts in this book. You will get better understanding if you combine reading this book alongside building large language models from scratch by Sebastian. additionally, the last four chapters that walk through the hands-on approaches regarding neccessary libraries to fine-tuning, quantization, distillation, inferences help me better comprehending on what the theoritical concepts explained in this book.

[Ferhat Culfaz · Rating 4 out of 5]

Great snippets of code to be able to deep dive specific latest concepts for LLMs from a practical perspective. Covers wide breadth. Not for reading cover to cover line by line but for people who already know the basics and have already some experience using LLMs. Good for keeping up with latest developments. Well worth going to the website to run the code on colab and also see the references.

[Nick · Rating 3 out of 5]

For the first AI textbook I read, I critiqued its lack of real-world code. Here, however, I found there was too much code. The information is nothing new for me, but the code is all dependent on Python and LangChain and of course it's all outdated now.

[Akshay Raut]

Great book for learning llm with llama-index

[Beth Fiore · Rating 5 out of 5]

Wow!

i feel lucky to have found. this book and all the resources it links to —this book covers everything and offers countless free resources to practice what you learn and to go more in depth on topics that you want to get more education on