A practical overview of how teams evaluate AI responses using benchmarks, human review, hallucination detection, red-teaming, and continuous testing.

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The article argues that AI correctness is multidimensional, covering factual accuracy, logical coherence, relevance, completeness, and calibrated confidence. It reviews several evaluation methods, including lexical and embedding-based metrics, LLM judges, standardized benchmarks, human annotation, hallucination detection, adversarial testing, and domain-specific expert review.