•1 min read•from Machine Learning
[P] Using YouTube as a data source (lessons from building a coffee domain dataset)
![[P] Using YouTube as a data source (lessons from building a coffee domain dataset)](/_next/image?url=https%3A%2F%2Fexternal-preview.redd.it%2FW1qtdlwVAgyDO8Gb7Jxbi3iDIoTqWN84efUix2wtWLs.png%3Fwidth%3D140%26height%3D70%26auto%3Dwebp%26s%3D7e7fa489ec45bfc1ae5cece40c791310376ce134&w=3840&q=75)
| 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:
It basically became the data layer for my app, and funnily ended up getting way more traction than my actual coffee coaching app! Repo: youtube-rag-scraper [link] [comments] |
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