~ / top-10 / ai-llm-engineering-books
AI · Top 10
AI & LLM engineering books
Books for engineers who want to build, evaluate and ship systems on large language models.
Draft — under review
- 01
AI Engineering
Chip Huyen
The best end-to-end guide to building applications on foundation models.
- 02
Designing Machine Learning Systems
Chip Huyen
Production ML from data to deployment and monitoring.
- 03
Build a Large Language Model (From Scratch)
Sebastian Raschka
Understand an LLM by writing one, step by step.
- 04
Hands-On Large Language Models
Jay Alammar & Maarten Grootendorst
Visual, practical introduction to how LLMs work and how to use them.
- 05
LLM Engineer’s Handbook
Paul Iusztin & Maxime Labonne
MLOps and LLMOps patterns for real deployments.
- 06
Building LLMs for Production
Louis-François Bouchard & Louie Peters
RAG, fine-tuning and evaluation for reliable applications.
- 07
Prompt Engineering for Generative AI
James Phoenix & Mike Taylor
Principles of prompting for text and image models.
- 08
Prompt Engineering for LLMs
John Berryman & Albert Ziegler
How LLM applications are designed around the prompt.
- 09
The AI Engineering Bible
Thomas R. Caldwell
A broad reference to the AI engineering lifecycle.
- 10
Deep Learning
Ian Goodfellow, Yoshua Bengio & Aaron Courville
The foundational textbook behind modern AI.