An answer without operational context is another thing to check.
A forecast can be statistically useful and still recommend an impossible response when it misses capacity, policy or a recent change in the operation.
Bring the relevant constraints and evidence into evaluation, so an operator can understand why an option is being considered—and where uncertainty remains.
Define the task and permitted context, then compare a rule, analytic method or model against representative cases. Record inputs, limitations and evaluation criteria. Escalate uncertain results for human review.
Core design considerations
Context selection
identify the evidence each evaluation is allowed to use.
Option comparison
expose tradeoffs and known constraints.
Evaluation
test quality, failure cases and fallback behavior before relying on outputs.
A workflow worth proving
Who can see, decide and act?
Agree what information can enter a model and who may inspect its output. A recommendation should not confer authority to change an operational system.
Fit the implementation to the environment.
Evaluate model endpoints, runtime location and data handling against the task. Compare quality and operating cost rather than assuming a single model fits every decision.
Agree what success would mean.
Use representative and adverse cases to assess recommendation quality, constraint violations and the rate of appropriate escalation.
Critical infrastructure: evaluate an intervention with its dependencies in view. ↗