Show HN: Annotate any document and train extraction by example, not prompts

Hi HN, 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.

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 often need far fewer than traditional ML approaches.

We’re also on Product Hunt today (currently #5), but feedback from HN is very appreciated.