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Books worth the weekend,
vetted by thousands of readers.
No fake reviews. No sponsored rankings. Just careful guides to the technical and non-fiction books that earn their place on a busy engineer's nightstand.
№ 01
Chip Huyen
AI Engineering
★ 4.4 · 899 ratings
№ 02
Chip Huyen
Designing Machine Learning Systems
★ 4.6 · 933 ratings
№ 03
Paul Iusztin, Maxime Labonne
LLM Engineer's Handbook
★ 4.5 · 184 ratings
№ 04
Sebastian Raschka
Build a Large Language Model (From Scratch)
★ 4.5 · 445 ratings
№ 05
Jay Alammar, Maarten Grootendorst
Hands-On Large Language Models
★ 4.5 · 392 ratings
№ 06
Louis-François Bouchard, Louie Peters
Building LLMs for Production
★ 4.8 · 23 ratings
№ 07
John Berryman, Albert Ziegler
Prompt Engineering for LLMs
★ 4.1 · 60 ratings
Browse all
39 more titles in the library →
Editor's Pick · April
AI Engineering
"The rare technical book that genuinely changes how readers think."
— Based on 899+ reader reviews
This week's most read
View all 39 → 01
Chip Huyen
AI Engineering
Building Applications with Foundation Models
★ 4.4 · 899 RATINGS
02
Chip Huyen
Designing Machine Learning Systems
An Iterative Process for Production-Ready Applications
★ 4.6 · 933 RATINGS
03
Paul Iusztin, Maxime Labonne
LLM Engineer's Handbook
Master the Art of Engineering Large Language Models from Concept to Production
★ 4.5 · 184 RATINGS
Index · By Subject
01
AI & ML Engineering
Books on building, deploying, and operating AI and machine learning systems in production. From data pipelines to model serving.
5 BOOKS
02
Large Language Models
Books on understanding, building, fine-tuning, and deploying large language models. From transformer internals to production LLM apps.
9 BOOKS
03
Prompt Engineering
Books on crafting effective prompts, building LLM-powered applications, and getting reliable outputs from AI models.
2 BOOKS
04
Machine Learning
Books covering classical machine learning, scikit-learn, and the foundations every developer needs before going deep on LLMs or deep learning.
3 BOOKS
05
Deep Learning
Books on neural networks, CNNs, RNNs, transformers, and generative models. The architecture-level understanding behind modern AI.
4 BOOKS
06
AI Agents
Books on building agentic AI systems that can plan, reason, use tools, and operate autonomously. The 2026 frontier of AI engineering.
2 BOOKS
07
AI Strategy & Society
Books on how AI is reshaping work, business, and society. For developers who want context beyond the code.
2 BOOKS
08
System Design & Distributed Systems
Books on designing reliable, scalable, and maintainable backend systems. Storage, replication, consensus, streaming, and the trade-offs behind every architectural choice.
7 BOOKS
09
Software Craft & Code Quality
Books on the daily craft of writing software — clean code, refactoring, design principles, and the habits that separate working code from code that lasts.
9 BOOKS
10
Engineering Practices & Culture
Books on how teams actually build software over time — code review, testing, version control, dependency management, and the practices behind long-lived codebases.
8 BOOKS
From the readers
"How to build production applications on top of foundation models"
— 899+ readers
on AI Engineering
"Production ML is 90% data engineering and 10% model development"
— 933+ readers
on Designing Machine Learning Systems
"How to build a complete LLM application pipeline from data to deployment"
— 184+ readers
on LLM Engineer's Handbook