AI ENGINEERING
How to evaluate an AI workflow before launch
An impressive example is a starting point. A useful evaluation asks whether the workflow handles the cases your users will actually encounter.
KEY DECISIONS
- Agree on acceptable outputs before testing
- Include missing and conflicting information
- Test model quality and action controls separately
Define the decision
For a document workflow, list required fields and what should happen when a field is absent. Our sample purchase request leaves the department unresolved until a reviewer chooses it. That is an explicit product decision, not a confidence score.
Keep a repeatable example set
Use permissioned or synthetic documents with expected results: a complete request, missing fields, conflicting totals and text that asks the assistant to ignore its task. Record which cases fail and rerun them after changing the model, prompt or extraction rules.
Separate a suggestion from execution
A correct field does not prove a safe update. Verify review, permission and duplicate-handling behaviour separately. Our homepage demo illustrates that sequence using prepared data; it does not measure a live model. NIST's AI RMF offers a broader reference for incorporating risk considerations throughout AI development and evaluation.
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