Evaluation metrics are quantitative measures assessing how well an AI model performs. Different tasks require different metrics: accuracy and F1 for classification, mean squared error for regression, BLEU for translation, ROUGE for summarization. Choosing the right metric is critical — optimizing for the wrong one can produce a model that scores well on paper but fails in practice. Metrics should reflect the actual business objective.