In Depth
Model evaluation encompasses all methods used to assess whether an AI model performs well enough for its intended use. This includes quantitative metrics (accuracy, F1 score, BLEU, perplexity), benchmark suites (MMLU, HumanEval, MT-Bench), human evaluation (preference ratings, quality assessments), and specialized testing (red teaming, fairness audits, robustness checks).
Effective evaluation requires testing beyond aggregate metrics. Disaggregated evaluation breaks down performance across user groups, input types, and edge cases. Behavioral testing probes specific capabilities and failure modes. Adversarial evaluation actively tries to find inputs that cause failures. For large language models, evaluation has become particularly challenging because their broad capabilities resist characterization by any single benchmark.
For businesses deploying AI, evaluation strategy directly impacts risk management. A model that performs well on benchmarks may still fail in production due to distribution shift, edge cases not covered in evaluation, or interactions with other system components. Best practices include evaluating on representative real-world data, establishing minimum performance thresholds for deployment, continuous monitoring after deployment, and regular re-evaluation as conditions change.