# How Rabbithole makes its AI lessons

Every Rabbithole lesson is written from an approved list of sources, and every factual claim in it traces back to one of them.

## An allowlist, not the open web

A source exists for the lessons only once it is on the list. The list has three kinds:

- Primary sources: official documentation from the companies that make the tools, research papers from the AI labs and universities, and official courses.
- Named researchers speaking: university lectures and talks by researchers with a verifiable affiliation.
- Explainers by researchers and lab engineering blogs, checked against a rubric.

Left out by default: listicles, SEO content, AI news channels, anything without a named author whose credentials can be checked, and summaries of a source we could read in the original.

## Every claim traced

No lesson, exercise, hint or answer ships unless every factual claim in it resolves to a passage in an approved source. A claim that can't be traced is dropped, not reworded. When sources disagree, the lesson says so.

## The WHY button

After an answer, tap WHY to see the source behind it.

## In our own words

Lessons teach in their own words, and every lesson links to the sources it was made from. Short examples name and link their source.

## No price lists

Prices change too often to teach. When a lesson needs another company's price, it points you to that company's own pricing page.

## The sources today

Today's 672 lessons draw on 384 sources: 332 primary sources, 31 lecture series and talks by named researchers, and 21 researcher-written explainers. The most used:

1. [Lil'Log](https://lilianweng.github.io/) (researcher explainer, 57 lessons)
2. [Lovable documentation](https://docs.lovable.dev/) (primary source, 37 lessons)
3. [Hugging Face LLM Course](https://github.com/huggingface/course) (researcher explainer, 36 lessons)
4. [LLMs from Scratch](https://github.com/rasbt/LLMs-from-scratch) (researcher explainer, 32 lessons)
5. [ElevenLabs documentation](https://elevenlabs.io/docs/overview/intro) (primary source, 29 lessons)
6. [Gemini API Documentation](https://ai.google.dev/gemini-api/docs) (primary source, 24 lessons)
7. [openai/openai-cookbook](https://github.com/openai/openai-cookbook) (primary source, 24 lessons)
8. [Supabase Documentation](https://supabase.com/docs) (primary source, 23 lessons)
9. [Claude Cookbooks](https://github.com/anthropics/claude-cookbooks) (primary source, 23 lessons)
10. [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course) (primary source, 23 lessons)
11. [The Illustrated Transformer (and series)](https://jalammar.github.io/illustrated-transformer/) (researcher explainer, 21 lessons)
12. [Modal Guide](https://modal.com/docs/guide) (primary source, 20 lessons)
13. [Transformer Circuits Thread](https://transformer-circuits.pub/) (primary source, 20 lessons)
14. [torchtitan: A PyTorch native platform for training generative AI models](https://github.com/pytorch/torchtitan) (primary source, 20 lessons)
15. [MIT 6.S191: Introduction to Deep Learning](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) (named researcher, 20 lessons)
16. [OpenAI Codex and ChatGPT desktop documentation (ChatGPT Learn)](https://learn.chatgpt.com/docs/quickstart) (primary source, 19 lessons)

Source page: https://rabbithole.pro/how-we-make-lessons