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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
45 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 45 → 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.
10 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
11
Software Security
Books on application and web security, threat modeling, and secure engineering. For developers who want to ship code that holds up against real attackers, not just pass a checklist.
6 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