How to Correct AI Extraction Errors Safely

Problem: Fix merchant, date, total, tax, currency, or category fields while preserving the original document and review history.

Case notes for reviewers correcting recognized expenses

The useful outcome in this case is to make corrections traceable and proportionate. Start from the source document and record only what another person can verify. A complete-looking row is not useful when its merchant, date, amount or context cannot be traced back to evidence.

What arrives

The process begins when you compare the field directly with the source. Before recognition, decide whether the file represents one transaction, several documents or an incomplete source.

What requires judgment

Next, determine whether the problem came from image quality or interpretation. The most relevant warning sign is changing several fields without rechecking. Keep that issue visible instead of allowing automation to turn uncertainty into an apparently final value.

What must survive handoff

Then correct the structured value without editing evidence, followed by recheck dependent totals and exports. The finished record should prove that the source supports the correction, dependent fields remain consistent, uncertainty is documented.

Article-specific walkthrough: How to Correct AI Extraction Errors Safely

Use this sequence as a dry run for reviewers correcting recognized expenses:

1. Opening condition: Compare the field directly with the source.
2. First evidence test: Verify that the source supports the correction; specifically investigate changing several fields without rechecking.
3. Mid-workflow decision: Determine whether the problem came from image quality or interpretation. Then correct the structured value without editing evidence without changing the original source.