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AI Evaluation Scorecard
Evaluate the whole AI workflow, not only the answer.
A model can sound confident and still fail the business. A useful scorecard checks whether the AI used the right sources, stayed inside its permissions, helped the user complete the workflow, and failed safely when needed.
Guide section
Quality categories
Score the behavior that matters to the actual workflow instead of relying on a single overall score.
- Task completion and answer usefulness
- Source grounding and citation strength
- Safe-tool routing and escalation behavior
- Staff and customer clarity
Guide section
Critical failure gates
Some failures should block promotion even if the rest of the score looks good.
- Live-action or unauthorized execution claims
- Private data or source leakage
- Unsupported factual claims
- Unsafe legal, financial, or compliance advice
Guide section
Promotion record
Every candidate should leave a record that explains the tested workflow, version, model or prompt change, known failures, and recommended disposition.
- Versioned test set and results
- Latency and reliability checks
- Browser and user-journey proof
- Known failures and next action
Interactive resource
Use the guide while you read.
These local controls turn the same resource into a checklist, scorecard, or planning board. Nothing is submitted, stored, or sent to a model.
Start here
Turn the guide into a first proof.
The best next step is a narrow workflow, visible evidence, and a plan your team can explain.