Analysis / Business automation and AI evaluation
Score AI extraction against reference fields
Score AI extraction in Proforma against reviewed reference fields. Separate missing, incorrect and unsupported values, with document-type failure examples.
AI automation lead
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Your starting prompt.
Evaluate [AI extraction workflow] against [reviewed reference fields] on [permitted documents]. Define field-specific matching and normalization rules before scoring, separating missing, incorrect and unsupported values. Show results by document type and field with actual failure examples. Preserve source references and the exact schema, prompt and model used. Do not reward plausible invented values or treat a format-valid object as semantically correct extraction.
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Method and review
Keep the assumptions visible.
Define inputs, outputs, permissions and failure conditions before implementing an Action. Evaluate AI against representative labeled examples and retain human approval for consequential steps.
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Reviewers should check the reference set and normalization rules before interpreting extraction quality.
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Before you begin
- Permitted documents with reviewed reference fields
- Configured AI runtime
- Agreed matching and normalization rules
Work with permitted files or queryable data. Verify the result against the original records.
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