How do I evaluate AI model quality?

AI model evaluation measures whether your model produces correct, useful, and safe outputs for your specific use case. Generic benchmarks (MMLU, HumanEval) tell you about general capability — your evaluation must test YOUR tasks. Build an evaluation dataset: collect 50-200 examples of real inputs and ideal outputs from your use case. Cover edge cases, not just happy paths. Include adversarial examples (inputs designed to trick the model). Run automated metrics: for classification tasks, use precision, recall, and F1 score. For generation tasks, use BLEU/ROUGE (word overlap with reference), or better, LLM-as-judge (use a stronger model to grade outputs on criteria you define: accuracy, tone, completeness, safety). Human evaluation: have domain experts rate a sample of outputs on a 1-5 scale across dimensions. This is expensive but catches issues automated metrics miss. A/B testing: deploy two model versions, randomly assign users, and measure real business outcomes (resolution rate, satisfaction score, conversion rate). Continuous monitoring: track hallucination rate (factual errors), refusal rate (model declining valid requests), latency (p50 and p99), and user feedback. Set alerts for regression.