Evaluation
AI Evaluation
The discipline of measuring an AI model's capability, safety, and alignment through benchmarks, human review, and red-teaming before and after release.
AI evaluation is the discipline of systematically measuring an AI model’s capability, safety, and alignment. Its goal is to establish objectively what a model can do, where it fails, and how risky it is — both before and after release.
Evaluation combines several methods. Standardized benchmarks score knowledge and skills against a fixed set of tasks. Human evaluation (A/B comparisons, response rating) captures quality that automated tests miss. A red team uses adversarial methods to surface jailbreaks and misuse, while hallucinations, sycophancy, and scheming are increasingly measured too. Holistic frameworks such as HELM track accuracy alongside bias, robustness, and toxicity.
In 2025–2026, evaluation has become central to managing AI safety and alignment. Labs publish “system cards” of results with every frontier model, OpenAI and Anthropic ran cross-evaluations of each other’s models, and the EU AI Act and national AI institutes make it a regulatory requirement. A core challenge remains benchmark saturation and contamination: a high score does not guarantee real-world reliability.