Model Evaluation
Systematically testing AI models and agents against benchmarks, domain tasks, and safety criteria before deployment.
What is Model Evaluation?
Model evaluation goes beyond accuracy scores. Enterprise evaluation tests for domain fitness, hallucination rate, bias, latency under load, compliance with output policies, and regression when models are updated. Without it, model swaps and routing changes are guesswork. See Agent Evaluation and Model Evaluation Suite.
What is an example of Model Evaluation?
Before releasing a support agent, a team tests task completion, citation support, refusal behavior, prompt injection, permission boundaries, tool accuracy, latency, cost, and escalation across real languages and issue types.
How is Model Evaluation different from related concepts?
A benchmark is a fixed dataset and scoring method. An evaluation is the broader evidence process used to decide whether a model or system is fit for a particular purpose.
What should enterprises evaluate for Model Evaluation?
- Translate business, safety, compliance, and user requirements into measurable pass criteria before comparing models.
- Use held-out and adversarial cases, record versions and seeds where possible, and investigate subgroup performance.
- Run regression tests on every material change and monitor production signals for failures the test set missed.
Read the full guide: Model Evaluation — in-depth article →
Related terms
Authoritative sources
Primary sources for the formal meaning, requirements, or original research behind Model Evaluation:
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