How do I prevent false positives and false negatives in document extraction?

Direct answer

False positives happen when the workflow extracts something that looks plausible but is not the right field. False negatives happen when the value exists but the workflow misses it.

The fix is not just a better model; it is a complete process with clear field definitions, document-specific instructions, validation checks, representative test files, and review thresholds.

What the workflow should include

Use expected data types, required-field checks, range checks, cross-field consistency checks, and exception handling so suspicious values do not move downstream automatically.

Review samples from new senders, changed formats, and long narrative documents because those cases usually reveal whether the workflow is resilient.

How Lido helps

Lido combines extraction with reviewable spreadsheet outputs and validation rules, so teams can catch errors before exporting data to operational systems.

That makes accuracy management an ongoing workflow instead of a one-time OCR template setup.

Example workflow

1. Define each field and include examples of correct and incorrect values.
2. Add validation for data types, formats, ranges, and required fields.
3. Test files that previously caused misses or wrong extractions.
4. Route uncertain or failed records to review before export.