Build a human correction loop for extracted documents

A correction interface should improve the current record and create trustworthy learning evidence. Automatically feeding every edit into training can import reviewer mistakes, personal data and evaluation leakage.

Separate corrections from training candidates

The adaptation lab records two synthetic reviews.

from adaptation_cases import correction_loop_case

result = correction_loop_case()
assert result["training_candidates"] == ["D1"]
assert result["original_preserved"] is True
assert result["automatic_training_ingest"] is False
assert result["audit_complete"] is True

D1 changes an incorrect total; D2 confirms an existing date. Both preserve reviewer and reason, but only the changed field becomes a candidate.

Design the review surface

Display the source page/region beside the proposed typed value. Highlight validation failures, not only low confidence. Let reviewers accept, correct, mark unreadable, request another source or escalate. Require a reason for critical changes and prevent the same person from approving restricted high-impact actions where separation is required.

Store original suggestion, corrected value, source version, reviewer, timestamp, reason, model/release and policy version. Apply the approved correction to the business record through an authorized, idempotent path. Audit access and minimize retained personal data.

Create a separate governed pipeline for learning use: quality review, rights/privacy check, deduplication, source grouping and split assignment. Keep holdout documents excluded. Measure correction rate, time and disagreement by field and layout; do not interpret every human edit as model error without adjudication.

The Generative & Agentic AI course links document review to provenance, evaluation and safe data iteration.

Exercise

Build a local review queue for ten fixture documents. Correct three fields, replay one approval to prove idempotency, and export a training-candidate manifest without raw documents.

Continue learning

This article is part of the Model adaptation and multimodal tasks sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Speech transcription evaluation beyond one accuracy number.
• Continue with Measure multilingual model quality by language.

Reference: NIST AI RMF Generative AI Profile.