K-shot training with LLMs

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

Comments

[Jess Lee]

Congrats! Good luck on the launch.

[Anton Loss]

Thank you!