How to Evaluate AI Document Extraction for Regulated Workflows
Evaluation criteria for AI document extraction when the output must support audit, review, and regulated operations.
AI document extraction is useful only when the reviewer can inspect and defend the output.
For regulated workflows, evaluation should go beyond field accuracy. You need to know whether the system preserves evidence, handles uncertainty, catches missing support, and routes exceptions correctly.
Evaluation criteria
| Criterion | Question to ask |
|---|---|
| Evidence | Does every extracted value link to a quote, page, or source span? |
| Sufficiency | Can the system decide whether the document proves the required fact? |
| Gaps | Does it find missing, expired, contradictory, or unreadable evidence? |
| Context | Does it use form answers, chat history, prior facts, and adjacent evidence? |
| Review | Can humans correct output without losing the audit trail? |
| Outage handling | Does failure become a review state instead of silent fallback? |
What not to optimize for
Do not optimize only for extraction speed. A fast unsupported value still costs time if an operator must verify it manually. Do not rely on confidence scores alone. A quote and page reference are more useful to a reviewer than a percentage.
The best benchmark is your own packet: actual documents, actual required facts, actual review outcomes.