K-shot training with LLMs

Anton Loss — Wed, 17 Sep 2025 20:37:06 +0000

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

Forem: Anton Loss

K-shot training with LLMs