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Valutazione del modello (Model Evaluation)
Model evaluation is the process of measuring how well an AI system performs the tasks it was designed for. It is not a single test but a set of quantitative and qualitative methods covering accuracy, robustness, safety and consistency of responses. It is used to compare different models, decide whether a model is ready for production, and spot weaknesses before they become real problems.
In practice, automated benchmarks, task-specific tests and human review of outputs are combined. The resulting scores are compared against reference thresholds or against other models' performance, giving a comparable picture rather than an isolated number. Evaluation is often repeated over time, since a model can degrade or improve with subsequent updates.
Companies use model evaluation to choose which AI to adopt, to certify product quality before release, and to monitor performance after deployment. It is also central in research, where new models are systematically compared against existing ones.
The term spread alongside the growth of large language models, when it became clear that performance could not be taken for granted and a shared vocabulary was needed to describe and compare it.
Grace applies model evaluation to its own scenarios: every answer users generate is measured against comparable criteria, so scores stay consistent regardless of the AI model chosen.
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