Becoming an AI engineer in 2025 is like trying to build a rocket using an IKEA manual – it is entirely possible, but you need the right tools. Here are ten books that not only teach you how to ask questions to ChatGPT, but also how to build, scale, and maintain entire AI systems. Prepare to become the person who truly understands what a transformer does.
Table of content
AI Engineering by Chip Huyen
If you are only going to read one book – make it this one. Chip Huyen, with experience from Netflix, NVIDIA and Stanford, guides you through the entire AI stack lifecycle: from data collection to deployment. It is like a survival guide for AI engineers who want to do more than just win Kaggle competitions.
The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne
This is the handbook for those who want to go from playing with GPT to building real LLM applications. It covers everything from prompt engineering to fine-tuning and production. The authors have experience building LLM apps at scale – it shows.
Designing Machine Learning Systems by Chip Huyen
Another gem from Huyen. This book focuses on how to design and operate machine learning systems in real-world situations, such as data operations and model reliability. Perfect for those who want to think like a product engineer, not just a model builder.
Building LLMs for Production by Louis-François Bouchard and Louie Peters
Want to know how to actually get an LLM into production? This book shows you how to fine-tune, deploy and maintain LLMs like a real engineer. Full of practical advice and architecture examples.
Build a Large Language Model (from Scratch) by Sebastian Raschka, PhD
Sebastian Raschka is a legend in machine learning. This book teaches you how to build a transformer-based LLM from scratch with PyTorch. You will understand the model at the code level – not just by using APIs.
Hands-On Large Language Models by Jay Alammar and Maarten Grootendorst
This book guides you through building and fine-tuning large language models with modern tools like Hugging Face Transformers and LangChain. Practical and hands on – ideal for developers and data scientists.
Prompt Engineering for LLMs by John Berryman and Albert Ziegler
If you build AI products with OpenAI Claude or open source LLMs this book shows you how to write smarter prompts for better results. It covers strategies like few shot prompting and chain of thought.
Building Agentic AI Systems by Anjanava Biswas and Wrick Talukdar
This book shows how to build agentic AI systems that can interact with environments reason make decisions and act. If you are interested in building AI agents like Auto GPT or BabyAGI this is your guide.
Prompt Engineering for Generative AI by James Phoenix and Mike Taylor
A comprehensive guide to prompt engineering techniques specifically designed for generative AI systems including text image and code generation. The book emphasizes how to write prompts that are robust and consistent.
The AI Engineering Bible by Thomas R. Caldwell
This book goes beyond models and APIs to show how to build real AI systems that are scalable and production-ready. From architecture to infrastructure, deployment to monitoring – it covers the entire AI lifecycle.
