Analysis / Business automation and AI evaluation
Evaluate AI classification on labeled cases
Evaluate AI classification in Proforma with reviewed labels and a fixed test set. Inspect per-class errors, ambiguous cases and limits of the measured result.
AI product lead
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Your starting prompt.
Evaluate [classification prompt] on [reviewed labeled examples] using [configured model]. Keep a development subset separate from a fixed evaluation subset, calculate per-class errors and inspect ambiguous labels. Show class imbalance, sample counts and failure examples rather than one aggregate accuracy alone. Retain prompt, model and case references. Do not report unavailable model runs as measured results or generalize performance beyond this evaluation set.
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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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Domain reviewers should validate labels and error costs; a small sample cannot establish universal classifier reliability.
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Before you begin
- Permission to use reviewed evaluation cases
- Configured AI runtime and identified model
- Reference labels and a preserved evaluation split
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