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Excited To Bring You the E-book Version of “Building LLMs for Production”
Artificial Intelligence   Latest   Machine Learning

Excited To Bring You the E-book Version of “Building LLMs for Production”

Last Updated on June 23, 2024 by Editorial Team

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

You asked. We listened.

Many of you asked for an electronic version of our new book, so after working out the kinks, we are finally excited to release the electronic version of Building LLMs for Production.”

Excited To Bring You the E-book Version of “Building LLMs for Production”

We’ve heard many feedback from you guys wanting to have both the e-book and book for different occasions. Think of this version as your “carry it wherever you go” AI toolkit. Enjoy enhanced accessibility and easy navigation!

Reading Experience

We know the digital reading experience will differ from the physical version, but we wanted to add something more. We worked hard to ensure it offered a more interactive experience and knowledge through the embedded links (great free learning references) we could add, especially for coding examples. We are excited to see how you use it to create exciting projects, tools, and more.

Find it now on Amazon as a paperback, high-quality colored hardcover, and e-book.

We have also released a paperback specifically for our community in India. Thanks to a partnership with Shroff Publishers, you can order it here!

About Building LLMs for Production

Generative AI and LLMs are transforming industries with their ability to understand and generate human-like text and images. However, building reliable and scalable LLM applications requires a lot of extra work and a deep understanding of various techniques and frameworks.

We concentrate on the LLM concepts from the ground up to advanced techniques. Most importantly, this book is a product of identifying the challenges we face in a production environment. So, it mainly focuses on practical solutions for tackling each roadblock.

The book is packed with theories, concepts, projects, applications, and experience that you can confidently put on your CVs. We also hope it is a great motivation for you to finish the book. And if you do, add this straight into your resume with confidence:

Large Language Models (LLMs) | LangChain | LlamaIndex | Vector databases | RAG | Prompting | Fine-tuning | Agents | Deployment & Deployment Optimizations | Creating chatbots | Chat with PDFs | Summarization | AI Assistants | RLHF

It is an end-to-end resource for anyone looking to enhance their skills, dive into the world of AI, or develop their understanding of Generative AI and large language models (LLMs).

We also want to address a question that often arises: “Aren’t you scared that the content will be out of date quickly?”

That’s something we thought a lot about. And the answer is no. We focused on the essentials and the foundational knowledge that will stay relevant even as libraries and models change and improve. Yes, LlamaIndex and LangChain will transform or even disappear, just as Tensorflow is no longer maintained. The principles of CNNs and early vision transformers are still important as a good background for ML engineers, even though they are much less popular nowadays. We believe it’s the same for the tech stack covered in the book. What we teach will remain worthwhile and a solid foundation for anyone working with LLMs in the future.

Amazing Early Feedback from AI Industry Leaders & Professionals

“This is the most comprehensive textbook to date on building LLM applications — all essential topics in an AI Engineer’s toolkit.”

— Jerry Liu, Co-founder and CEO of LlamaIndex

“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.”

— Shashank Kalanithi, Data Engineer at Meta

“A truly wonderful resource that develops understanding of LLMs from the ground up, from theory to code and modern frameworks.”

— Pete Huang, Co-founder of The Neuron

“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.”

— Jeremy Pinto, Senior Applied Research Scientist at Mila

“The book is accessible, with multiple tutorials that you can readily copy, paste, and run on your local machine to showcase the magic of modern AI.”

— Rafid Al-Humaimidi, Senior Software Engineer at Amazon Web Services (AWS)

Your Early Movers Advantage

LLMs such as GPT-4 often lack domain-specific knowledge, making generating accurate or relevant responses in specialized fields challenging. They can also struggle with handling large data volumes, limiting their utility in data-intensive scenarios. Another critical limitation is their difficulty processing new or technical terms, leading to misunderstandings or incorrect information. Hallucinations, where LLMs produce false or misleading information, further complicate their use. Hallucinations are a direct result of the model training goal of the next token prediction — to some extent, they are a feature that allows “creative” model answers. However, it is difficult for an LLM to know when it is answering from memorized facts or imagination. This creates many errors in LLM-assisted workflows, making them difficult to identify. Alongside hallucinations, LLMs sometimes also simply fail to use available data effectively, leading to irrelevant or incorrect responses.

The book focuses on adapting large language models (LLMs) to specific use cases by leveraging Prompt Engineering, Fine-Tuning, and Retrieval Augmented Generation (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.

LLMs are currently most often used in production as chatbots or for performance and productivity-enhancing “copilot” use cases, with a human still entirely in the loop rather than for fully automated tasks due to these limitations. But there is a long journey from a basic LLM prompt to sufficient accuracy, reliability, and observability for a target copilot use case.

We think the key developer tool kit for LLM-based products is 1) Prompt Engineering, 2) Retrieval Augmented Generation (RAG), 3) Fine-Tuning, and 4) Custom UI/UX.

Parts of this toolkit will be partially integrated into the next generation of foundation models, while parts will be solved through added frameworks like Llamaindex and Langchain, especially for RAG workflows. However, the best solutions will need to be tailored to specific industries and applications. We also believe prompting and RAG are here to stay — and over time, prompting will resemble the necessary skills for effective communication and delegation to human colleagues.

Order your copy of Building LLMs for Production right away!

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