Eval Set
A core tool for AI feature acceptance testing, consisting of a curated collection of input samples paired with expected outputs or scoring criteria. It is used to systematically measure model performance on specific tasks and must cover typical scenarios, edge cases, and high-risk inputs.
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Because Agent outputs are non-deterministic, simple unit tests are insufficient to guarantee quality; it is necessary to maintain a regression evaluation set to quantitatively compare key metrics before and after releases.
A regression evaluation set (Eval Set) typically contains representative input cases, the corresponding expected tool call sequences (golden traces), and acceptable output ranges.
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Because Agent outputs are non-deterministic, simple unit tests are insufficient to guarantee quality; it is necessary to maintain a regression evaluation set to quantitatively compare key metrics before and after releases.
50%UnverifiedA regression evaluation set (Eval Set) typically contains representative input cases, the corresponding expected tool call sequences (golden traces), and acceptable output ranges.
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