Human Evaluation

Human evaluation collects judgments from annotators on model output quality along dimensions like accuracy, helpfulness, clarity, and safety. It remains the gold standard because humans can catch subtle reasoning errors, cultural nuances, and novel failure modes that automated metrics miss. Human evaluation is expensive and slow, so it is typically reserved for model release decisions, calibrating automated judges, and diagnosing failure categories surfaced by cheaper evals.