Verified65% confidenceConceptExact time
AI testing introduces the concept of Evaluation, which typically uses continuous values from 0 to 1 for scoring, where evaluation is responsible for scoring and testing is responsible for setting the passing threshold.
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65%
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Long-term
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9/22/2026
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UnverifiedEvaluation datasets are the infrastructure for measuring the effectiveness of AI workflows, usually consisting of a batch of representative input samples with human-annotated correct answers, covering common cases and edge cases.78% similarVerifiedEvaluating AI should be done on a task-specific basis, focusing on reliability and how much human verification is required, rather than applying blanket optimistic or pessimistic judgments.77% similarVerifiedClearly defining acceptance criteria before handing a task to an AI allows the model to perform self-checking during generation, reducing output ambiguity.76% similarVerifiedThe AI automated review mechanism in essence embeds review rules within the Skill prompt, having the large model verify the output from a second perspective; this practice is referred to as self-criticism or a reflection loop.75% similarUnverifiedThe core transformation in AI testing lies in moving from 'assertions' (Test) to 'evaluation' (Evaluation), which is an internationally accepted practice.75% similar
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