Define failure in operational terms
Start with a specific behavior: a missed machine fault, an invalid transaction state, or a support request routed incorrectly. Describe its conditions and consequences before selecting a generation approach.
Create scenario families
Vary one meaningful dimension at a time, then consider interactions. Define normal cases, boundary conditions, and unusual combinations. Keep a record of assumptions, especially when real observations are scarce.
Separate coverage from prevalence
An evaluation set can deliberately contain more rare events than production. Label that choice and avoid treating its class proportions as a prediction of real event frequency. Training and evaluation may need different sampling strategies.
Validate domain plausibility
Ask domain specialists to review assumptions and impossible combinations. Test constraints and compare against available observations. Synthetic examples should help investigate a gap, not turn an unverified assumption into evidence.
Make the test repeatable
Keep an approved regression set with its configuration and review record. Add new scenario families when failures emerge. Compare model versions against a stable baseline as well as new challenges.
Have a specific requirement? Book a meeting with RoboSynth to discuss your dataset and validation needs.