A community-driven hub of 42+ runnable notebooks covering RAG techniques from foundational to cutting-edge - the intuition, the code, and the references to build more accurate, context-rich retrieval systems.

Prompt to Production - my full course on building software with AI the way professionals do: the methods and paradigms behind reliable, efficient, modular production systems, taught systematically. 17 modules, each pairing a video lecture with a hands-on lab, from your first structured prompt to a working production system.

The course is live. Every module is out, lecture and lab.

Your coding agent starts every session knowing nothing about your project, so it guesses. Paste one line into the agent you already have open, and about fifteen minutes later your repository has a docs layer written from the code itself, plus a card scoring what your agent knew before and after.

We ran it on six repositories you already depend on. Each one was asked five questions about itself, cold, then again after the layer was written. The last column counts statements in that project's own documentation that its own code disproves:

repo	before	after	own docs its code disproves
fastapi	2 of 5	5 of 5	2
flask	3 of 5	5 of 5	6
django	3 of 5	4 of 5	1
express	2 of 5	4 of 5	4
requests	2 of 5	4 of 5	0
langchain	5 of 5	4 of 5	5

Flask's six include four documentation examples that raise TypeError when you run them. Langchain scored lower afterwards, because it already ships a 380-line agent instruction file and the cold read was grading theirs; that row is in the table anyway.

Clone any of those repos, paste the same line, and check the number yourself. No signup.