[P] Using YouTube as a data source (lessons from building a coffee domain dataset)

Reddit r/MachineLearning / 3/30/2026

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Key Points

The author built a coffee coaching app and found that YouTube’s expert brew-method content is high-quality but not directly usable for RAG because transcripts require heavy preprocessing.
They report that transcript quality issues, inconsistent chunking, and cleaning steps made turning YouTube content into an embedding-ready dataset more time-consuming than expected.
To address this, they created a CLI tool that pulls videos from a channel, extracts transcripts, and cleans and chunks them for use with embeddings.
The project’s GitHub repo, youtube-rag-scraper, is positioned as the data layer for their RAG workflow and reportedly gained more traction than the original app.

[P] Using YouTube as a data source (lessons from building a coffee domain dataset)

I started working on a small coffee coaching app recently - something that could answer questions around brew methods, grind size, extraction, etc.

I was looking for good data and realized most written sources are either shallow or scattered. YouTube, on the other hand, has insanely high-quality content (James Hoffmann, Lance Hedrick, etc.), but it’s not usable out of the box for RAG.

Transcripts are messy, chunking is inconsistent, getting everything into a usable format took way more effort than expected.

So I made a small CLI tool that: