Building LLMs for Production is now available as an e-book! Start learning now!
What's Inside this 470-page Book (Updated October 2024)?
Please note: this e-book is an interactive resource, not a downloadable PDF.
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Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning
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Roadmap for Building Production-Ready Applications using LLMs
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Fundamentals of LLM Theory
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Simple-to-Advanced LLM Techniques & Frameworks
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Code Projects with Real-World Applications
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Colab Notebooks that you can run right away
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Community access and our own AI Tutor
Industry Leaders on the Book
“This is the most comprehensive textbook 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 Mila
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Chapter Overview
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Introduction
FREE PREVIEW -
Why Prompt Engineering, Fine-Tuning, and RAG?
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Coding Environment and Packages
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A Brief History of Language Models
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What are Large Language Models?
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Building Blocks of LLMs
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Tutorial: Translation with LLMs (GPT-3.5 API)
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Tutorial: Control LLMs Output with Few-Shot Learning
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Recap
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Understanding Transformers
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Transformer Model’s Design Choices
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Transformer Architecture Optimization Techniques
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The Generative Pre-trained Transformer (GPT) Architecture
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Introduction to Large Multimodal Models
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Proprietary vs. Open Models vs. Open-Source Language Models
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Applications and Use-Cases of LLMs
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Recap
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Understanding Hallucinations and Bias
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Reducing Hallucinations by Controlling LLM Outputs
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Evaluating LLM Performance
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Recap
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Prompting and Prompt Engineering
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Prompting Techniques
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Prompt Injection and Security
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Recap
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Who is it for?
- $29.99
- AI Practitioners & Programmers Tinkerers
- AI/ML Engineers & Computer Science Professionals
- Students/Researchers & Job Seekers
What Our Readers Say
I'm learning lots
Mark Chase
I am early on in the book. However, I can tell it will be a great experience. The concepts are clearly explained, and the sample code really helps reinforce the material. EDIT - I just finished the book. The learning experience continued to b...
Read MoreI am early on in the book. However, I can tell it will be a great experience. The concepts are clearly explained, and the sample code really helps reinforce the material. EDIT - I just finished the book. The learning experience continued to be great. I now need to develop a project and put this all together.
Read LessWell worth a read
Frederick Zhang
If you have aspirations to dive into the world of generative artificial intelligence (GenAI) and large language models (LLMs), you could definitely do worse than starting with this book. As the title implies, it is focused on building and thus rel...
Read MoreIf you have aspirations to dive into the world of generative artificial intelligence (GenAI) and large language models (LLMs), you could definitely do worse than starting with this book. As the title implies, it is focused on building and thus relatively light on theory. It teaches you what you need to know behind the scenes but not much more than that. For instance, there is almost no math. The tutorials and code samples are the highlight of the book, as they exemplify how the literature is actually put into practice. As with any field, the more knowledge you already possess coming into this book, the less value you will find in reading it. However, everyone should find something worthwhile. One area of this book I feel could really be improved upon is the section on deployment. Running a GenAI app locally on your laptop is a very different game from running it in production in terms of scalability. An app that runs smoothly for ten users will incur previously unseen issues when deployed for ten thousand and will incur even more issues for ten million. Scaling, debugging and troubleshooting in production deserve more attention than is given in this book for it to maximize its value for professional AI engineers. Overall, it is well worth a thorough reading and should prove to be of aid to your career if you wish to step into this field.
Read LessA Practical Guide to Building LLMs
Brian Langrin
"Building LLMs for Production" is an invaluable guide for anyone looking to deploy large language models efficiently and effectively. What sets this book apart is its all-in-one approach, covering everything from model architecture and optimizatio...
Read More"Building LLMs for Production" is an invaluable guide for anyone looking to deploy large language models efficiently and effectively. What sets this book apart is its all-in-one approach, covering everything from model architecture and optimization to scaling and deployment—all in a clear, accessible format that both beginners and experts can appreciate. The authors take a truly user-centric perspective, ensuring that practical implementation remains front and center. Whether you're integrating LLMs into existing workflows or building from scratch, this book simplifies complex concepts while maintaining technical depth. For developers and AI practitioners looking for a comprehensive, no-fluff resource, "Building LLMs for Production" is the go-to playbook for modern AI deployment. Highly recommended!"
Read LessA very good read on Large language models
Jamshaid Sohail
I am super excited to recommend this book to everyone. Written in a very excellent manner and covering all the essential details and concepts in the world of large language models. The complex and difficult concepts are easily graspable and the te...
Read MoreI am super excited to recommend this book to everyone. Written in a very excellent manner and covering all the essential details and concepts in the world of large language models. The complex and difficult concepts are easily graspable and the text is fully focused and coherent. Highly recommended book for academia as well as industry people.
Read LessOutstanding Book useful for both LLM Industry, academic and private projects
James Odendal
I recently read Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG, and I couldn’t be more satisfied with the insights and practical knowledge it provided. As someone involved in building rob...
Read MoreI recently read Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG, and I couldn’t be more satisfied with the insights and practical knowledge it provided. As someone involved in building robust AI-driven solutions, I found this book incredibly useful. It breaks down complex concepts like prompting strategies, fine-tuning techniques, and Retrieval-Augmented Generation (RAG) into manageable, actionable steps. The explanations are clear, and the examples are practical and relevant to real-world applications. This book is a must-have for anyone looking to take their LLMs from experimental stages to reliable, production-ready tools. Highly recommended!
Read LessBest one that accumulates all knowledge
Abhijit L
Top-notch book for getting into building LLM apps
Paul Iusztin
If you are interested in building AI apps, this book serves as a fantastic icebreaker, being one of the few within the AI space worth your time and money.
If you are interested in building AI apps, this book serves as a fantastic icebreaker, being one of the few within the AI space worth your time and money.
Read Lessexcelent book highly recommended
Roberto Pardo
nice balance between intuitive explanations and code
nice balance between intuitive explanations and code
Read LessExcellent course for beginners and Experienced persons
Vinay Gupta
This is an excellent book and I would like to recommend this book to everyone. I was having little idea about how to build an application and plan for production deployment but after going through this book , I came across the various techniques t...
Read MoreThis is an excellent book and I would like to recommend this book to everyone. I was having little idea about how to build an application and plan for production deployment but after going through this book , I came across the various techniques to take into account to develop application and its deployment aspects. This book is worth every penny.
Read LessSimply a must-read
Eugenio Galioto
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!
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 LessBest book on the topic
Hiroto Matsushima
The GO-TO guy about AI and LLM
The GO-TO guy about AI and LLM
Read LessBrilliant book
soumen nayak
Covers every aspect of a broad range of topics.
Covers every aspect of a broad range of topics.
Read LessThe Only AI Engineering Toolkit You Need!
To build scalable and reliable products with LLMs
LLM Fundamentals, Architecture, & LLMs in Practice
Foundations
- Building blocks of LLMs: language modeling, tokenization, embeddings, emergent abilities, scaling laws, context size…
- Transformer Architecture: attention mechanism, design choices, encoder-only transformers, decoder-only transformers, encoder-decoder transformers, GPT Architecture, Masked Self-Attention, MinGPT
LLMs in Practice
- Hallucinations & Biases: Mitigation strategies, controlling LLM outputs
- Decoding methods: greedy search, sampling, beam search, top-k sampling, top-p sampling
- Objective functions and evaluation metrics: perplexity metric and GLUE, SuperGLUE, BIG-Bench, HELM, FLASK Benchmarks…
Prompting & Frameworks
Prompting
- Prompting techniques: zero-shot, in context, few-shot, role, chains, and chain-of-thought…
- Prompt Injection and Prompt Hacking
Frameworks
- LangChain: prompt templates, output parsers, summarization chain, QA chains
- LlamaIndex: vector stores, embeddings, data connectors, nodes, indexes
RAG & Fine-Tuning
Retrieval-Augmented Generation Components
- Data Ingestion(PDFs, web pages, Google Drive), text splitters, LangChain Chains
- Embeddings, Vector Stores with Activeloop's Deep Lake
- Querying in LlamaIndex: query construction, expansion, transformation, splitting, customizing a retriever engine…
- Reranking Documents: recursive, small-to-big
- RAG Metrics: Mean Reciprocal Rank (MRR), Hit Rate, Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG)...
- Evaluation Tools: evaluating with ragas, custom evaluation of RAG pipelines
Fine-Tuning Optimization Techniques
- LoRA, QLoRA, supervised fine-tuning, SFT RLHF
Agents, Optimization & Deployment
Agents
- Using AutoGPT & BabyAGI with LangChain
- Agent Simulation Project: CAMEL, Generative Agents
- Building Agents, LangGPT, OpenAI Assistants
Optimization & Deployment
- Challenges, quantization, pruning, distillation, cloud deployment, CPU and GPU optimization & deployment, creating APIs from open-source LLMs
More Towards AI's Book Readers' Reviews
Comprehensive Coverage and Practical
Priyankar Kumar
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 LessSimply a must-read
Eugenio Galioto
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!
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 LessBest book on the topic
Hiroto Matsushima
The GO-TO guy about AI and LLM
The GO-TO guy about AI and LLM
Read LessFAQ
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What skills do I learn?
The 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
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What are the prerequisites to read the book?
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.
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How do we make sure the book is not outdated?
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.
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Does it come with a physical copy?
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!
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Do you have a referral or affiliate program?
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.
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Can I take this course within my company?
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].