An AI model does not exist as a static artifact. It lives within a dynamic ecosystem of changing data streams, evolving user behavior, regulatory constraints, and environmental shifts. The model lifecycle encompasses training, deployment, monitoring, drift detection, retraining, and re-validation. Evaluation is the nervous system running through all of it.

Evaluation itself is multi-dimensional: statistical accuracy, factual grounding, consistency, bias detection, hallucination rate, tone appropriateness, latency, and domain-specific correctness. Modern systems go further by using cross-model comparison, AI-as-a-judge review, human calibration loops, and automated scoring pipelines. Together, these practices create a continuous feedback loop where models do not merely operate - they improve.

A mature enterprise ecosystem treats evaluation not as a KPI, but as a core infrastructure layer.