Editor's top LLM Engineering books
4LLM Engineer's Handbook
Paul Iusztin, Maxime Labonne · Packt · 523 pages · ~13 hrs to read
The definitive LLM engineering book — covers the complete ML system lifecycle from data collection through fine-tuning, RAG, evaluation, and deployment. The most production-focused treatment of building AI applications available.
Build a Large Language Model (From Scratch)
Sebastian Raschka · Manning · 366 pages · ~9 hrs to read
Implements GPT from scratch in PyTorch — the deepest possible understanding of how LLMs work at the architecture level. Essential for engineers who need to debug model behaviour or make informed fine-tuning decisions.
Hands-On Large Language Models
Jay Alammar, Maarten Grootendorst · O'Reilly · 449 pages · ~11 hrs to read
Covers the full LLM engineering stack — text classification, embeddings, semantic search, fine-tuning, and text generation — with visual explanations and hands-on code. The best balance of conceptual clarity and practical implementation.
AI Engineering
Chip Huyen · O'Reilly · 535 pages · ~13 hrs to read
The most current and widely read book on building applications with foundation models — O'Reilly's most-read title on its platform in 2025. Huyen distinguishes AI engineering from traditional ML engineering explicitly, covering evaluation, RAG, and agents with the same production rigor as her earlier Designing Machine Learning Systems.
All LLM Engineering Books
6LLM Engineer's Handbook
Paul Iusztin, Maxime Labonne · Packt · 523 pages · ~13 hrs to read
The definitive LLM engineering book — covers the complete ML system lifecycle from data collection through fine-tuning, RAG, evaluation, and deployment. The most production-focused treatment of building AI applications available.
Build a Large Language Model (From Scratch)
Sebastian Raschka · Manning · 366 pages · ~9 hrs to read
Implements GPT from scratch in PyTorch — the deepest possible understanding of how LLMs work at the architecture level. Essential for engineers who need to debug model behaviour or make informed fine-tuning decisions.
Hands-On Large Language Models
Jay Alammar, Maarten Grootendorst · O'Reilly · 449 pages · ~11 hrs to read
Covers the full LLM engineering stack — text classification, embeddings, semantic search, fine-tuning, and text generation — with visual explanations and hands-on code. The best balance of conceptual clarity and practical implementation.
Designing Machine Learning Systems
Chip Huyen · O'Reilly0
The production ML systems reference — covers data pipelines, feature engineering, training, deployment, and monitoring for ML systems. Essential context for LLM engineers who need to build systems that operate reliably in production.
AI Engineering
Chip Huyen · O'Reilly · 535 pages · ~13 hrs to read
The most current and widely read book on building applications with foundation models — O'Reilly's most-read title on its platform in 2025. Huyen distinguishes AI engineering from traditional ML engineering explicitly, covering evaluation, RAG, and agents with the same production rigor as her earlier Designing Machine Learning Systems.
Natural Language Processing with Transformers
Lewis Tunstall, Leandro von Werra & Thomas Wolf · O'Reilly · 430 pages · ~11 hrs to read
Written by Hugging Face engineers, this is the foundational text for understanding the transformer architecture every LLM is built on, plus practical fine-tuning, distillation, and deployment with the Transformers library. Essential background for LLM engineers who need to reason about models rather than just call APIs against them.
LLM Engineering resources