A large collection of ready-made LLM apps: agents, RAG systems and skills for coding agents. Every template can be run and taken apart piece by piece.
Awesome lists & learningchiphuyen
aie-book
Companion materials for Chip Huyen's book AI Engineering: table of contents, chapter summaries, study notes, prompt examples and case studies. The book covers adapting foundation models to real tasks.
Who it is for
For people building products on foundation models who want a systematic view.
How to start
- Open
ToC.mdto see the book's table of contents - Read the chapter overviews in
chapter-summaries.md - Check
resources.mdandcase-studies.mdfor links and examples
Steps are taken from the README. Check the current version in the repository before running them.
Stars over the last 30 days
Author's description
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
More in «Awesome lists & learning»
A free 21-lesson Microsoft course that goes from generative AI basics and prompting to working applications. Each lesson ships with code.
Code for Sebastian Raschka's book: you build a GPT-like model in PyTorch step by step, then pretrain and finetune it. The best way to see how an LLM works inside.
An animation engine Grant Sanderson uses to make 3Blue1Brown videos, including the well-known neural network ones. It is not an AI library but a tool for explaining math and ML visually.
A 12-week course on classic machine learning: 26 lessons and 52 quizzes. The foundation that makes modern models easier to understand.
A three-part LLM roadmap: fundamentals, the LLM Scientist (building models) and the LLM Engineer (building apps). Topics come with Colab notebooks.
