File: 10 October 2026 · Source date: 10 October 2026
Test context
A model may score high on exam questions, but fail when faced with the language, errors, and extremes of real users. The assessment should start with the intended use and the people who may be harmed.
Measurement layers
The NIST approach recommends measuring technical performance as well as dimensions such as security, resilience, privacy, explainability, and bias. A single average score can hide these different risks.
Post-stream monitoring
Older tests may become invalid as data distribution, user behavior, and model version change. Record keeping is part of the incident notification and reassessment use cycle.
discussion question
What failure would you measure before success for an AI you use?
Resources and further reading
NIST — AI Risk Management Framework ↗NIST — AI Test, Evaluation, Validation and Verification ↗Share your answer
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