I built a tool for teaching LLMs how to extract structured data from documents by annotating, not prompt engineering. I’d love your feedback.
How it works:
- Upload a document (DOCX, PDF, image, etc.)
- Select and tag parts of it (supports nesting, arrays, custom tag structures)
- Upload another document → click "predict" → see editable annotations
- Amend them and save as a new example - Call the API with a third document → get JSON back
Use cases:
- Identify "important clauses" in contracts
- Extract "total value" from invoices
- Anything subjective, like "healthy ingredients" on a label
- Anything objective, like "postcode" or "phone number"
- You could even tag things like "good rhymes" in a poem — basically anything an LLM can understand and extrapolate
The key idea: instead of iterating endlessly on prompts (and sometimes regressing), you just iterate on examples. Each example improves accuracy in a concrete way, and you need far fewer than traditional ML approaches.
We’re launching on Product Hunt today (currently #5)
https://www.producthunt.com/products/deeptagger
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