Building LLMs for Production
Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG
Please note: this e-book is an interactive resource, not a downloadable PDF.
Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning
Roadmap for Building Production-Ready Applications using LLMs
Fundamentals of LLM Theory
Simple-to-Advanced LLM Techniques & Frameworks
Code Projects with Real-World Applications
Colab Notebooks that you can run right away
Community access and our own AI Tutor
Use code: CYBER_MONDAY_2024 at checkout for 15% off and start learning right away at your own pace!
“This is the most comprehensive...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.”
Jerry Liu, Co-founder and CEO of LlamaIndex“A truly wonderful resource that develops understanding of LLMs from the ground up, from theory to code and modern frameworks. Grounds your knowledge in research trends and frameworks that develop your intuition around what's coming. Highly recommend.”
Pete Huang, Co-founder of The Neuron“An indispensable guide for anyone venturing into the world of large language models...Covering everything from theory to practical deployment, it’s a must-have in the library of every aspiring and seasoned AI professional.”
Shashank Kalanithi, Data Engineer at Meta“It contains thorough explanations and code for you to start using and deploying LLMs, as well as optimizing their performance. Very highly recommended!”
Luis Serrano, PhD, Founder of Serrano.Academy and author of Grokking Machine Learning“It covers the foundational aspects of LLMs as well as advanced use-cases like finetuning LLMs, Retrieval Augmented Generation and Agents. This will be valuable to anyone looking to dive into the field quickly and efficiently.”
Jeremy Pinto, Senior Applied Research Scientist at MilaTable of Contents
About The Book
Introduction
Why Prompt Engineering, Fine-Tuning, and RAG?
Coding Environment and Packages
A Brief History of Language Models
What are Large Language Models?
Building Blocks of LLMs
Tutorial: Translation with LLMs (GPT-3.5 API)
Tutorial: Control LLMs Output with Few-Shot Learning
Recap
Understanding Transformers
Transformer Model’s Design Choices
Transformer Architecture Optimization Techniques
The Generative Pre-trained Transformer (GPT) Architecture
Introduction to Large Multimodal Models
Proprietary vs. Open Models vs. Open-Source Language Models
Applications and Use-Cases of LLMs
Recap
Understanding Hallucinations and Bias
Reducing Hallucinations by Controlling LLM Outputs
Evaluating LLM Performance
Recap
Prompting and Prompt Engineering
Prompting Techniques
Prompt Injection and Security
Recap
To build scalable and reliable products with LLMs
Foundations
LLMs in Practice
Prompting
Frameworks
Retrieval-Augmented Generation Components
Fine-Tuning Optimization Techniques
Agents
Optimization & Deployment
The book has great coverage of nearly all the important topics related to LLMs and application-building with LLMs. I also liked the focus on the hands-on projects, so that you are not just reading but also trying things out.
The book has great coverage of nearly all the important topics related to LLMs and application-building with LLMs. I also liked the focus on the hands-on projects, so that you are not just reading but also trying things out.
Read LessThe second version of this book can easily be considered a must-read as well as the first version. It's great to have key and evolving concepts explained like this!
The second version of this book can easily be considered a must-read as well as the first version. It's great to have key and evolving concepts explained like this!
Read LessThe book is packed with theories, concepts, projects, applications, and experience that you can confidently put on your CVs. You can add these skills straight into your resume: 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
The is written for readers without prior knowledge of AI or NLP. It introduces topics from the ground up, aiming to help you feel comfortable using the power of AI in your next project or to elevate your current project to the next level. A basic understanding of Python helps comprehend the code and implementations, while advanced use cases of the coding techniques are explained in detail in the course.
We ensure the book remains relevant by focusing on the core principles of building production products with LLMs, which are foundational and transferable across generations of models. While the field is fast-evolving and new techniques will emerge, today's LLM developer stack will still be crucial for adapting future models to specific industries and data. Additionally, we provide access to an up-to-date webpage with extra content, code, notebooks, and resources, ensuring readers stay current with the latest advancements.
No. This e-book version is hosted on the platform (not a pdf). You can purchase a soft or hard copy of the book on Amazon (https://amzn.to/4bqYU9b). If you have a physical copy, email Louis-François at [email protected], and we'd be happy to give you a discount on the e-book!
If you refer three or more people, we’ll send you a physical copy of our book as a thank you! Additionally, we have an affiliate program for individuals with an audience. By joining, you can earn commissions for every successful referral made through your unique affiliate link. Please email Louis-François at [email protected] with proof of referral.
Yes! We offer both course bundles and custom training solutions tailored specifically for companies. For more information on company packages or to discuss a customized training plan, reach out to Louis at [email protected].