How do you verify LLM document extraction accuracy?
Without a labeled ground-truth set, you verify an extraction two ways: provenance (where on the page did each value come from?) and calibrated confidence (how sure is the model, measured not guessed?). General LLMs give you neither. Verifiable-output models like Interfaze return both, so a human or a rule can flag low-confidence fields for review.
Last updated 2026-07-20
Why 'it looked right' isn't verification
A general LLM's output is fluent and plausible even when it's wrong, and it gives you no signal about which fields to double-check. Eyeballing a few results doesn't scale and misses exactly the confident errors that hurt.
Verify by provenance
A bounding box tells you a value was actually read from a specific spot on the page rather than hallucinated. If a field has no box and no matching text in the OCR output, nothing on the document supports it — a strong signal to review or reject it.
Verify by calibrated confidence
A real per-field confidence score lets you auto-route anything below a threshold to human review — the standard pattern in KYC and lending pipelines. With Interfaze the confidence and bounds arrive in `precontext` on the same OpenAI-style call, so you can threshold on them directly.
FAQ
How do I know an extracted field wasn't hallucinated?
Check its bounding box. No box and no OCR text match means nothing on the page supports the value, so it should be reviewed.