Validate AI models across task performance, robustness, hallucination behavior, calibration, regression, operational limits, and deployment readiness.
Select the model profile and deployment context. The tool will recommend the most important validation dimensions.
Planning aid only; not a certification or legal assessment.
Generate a plan to see recommended validation dimensions.
A strong validation program combines benchmark evidence with robustness, regression, operational, and use-case-specific testing.
| Dimension | Question | Evidence | Typical failure |
|---|---|---|---|
| Task performance | Does the model perform well on the intended task? | Representative benchmarks, task metrics, human review | Strong public benchmark, weak domain performance |
| Robustness | Does performance hold under variation and noise? | Prompt variants, perturbations, edge cases | Sharp degradation under small input changes |
| Hallucination / grounding | Are claims supported when evidence is required? | Grounded QA, citation checks, factuality tests | Confident unsupported outputs |
| Calibration | Does confidence reflect actual correctness? | Reliability curves, abstention tests, confidence analysis | High confidence on wrong answers |
| Regression | Did a new version break important behavior? | Versioned regression suite | Improvement on one metric with hidden degradation elsewhere |
| Operational limits | Can the model meet production constraints? | Latency, throughput, memory, context, cost | Good quality but unusable production characteristics |
| Safety boundaries | Does the model remain within defined constraints? | Policy tests, adversarial tests, refusal analysis | Unsafe behavior on uncommon inputs |
| Reproducibility | Can the result be repeated and explained? | Versioned config, datasets, prompts, metrics | Scores without enough context to reproduce them |