In 30 seconds
Linear engineer Jacob describes growing an AI-agent evaluation dataset through real use, combining deterministic checks with a language-model judge for more subjective output quality.
- 1Grow the test setUnexpected real-world use becomes new evaluation data over time.
- 2Check objective behaviorA request for an in-progress status should result in the corresponding status.
- 3Judge subjective outputThe team uses a language-model judge for questions such as whether the title and description capture the right information.
Takeaways
- Add unexpected user interactions to an evaluation dataset as the product is used.
- Check deterministic requirements, such as setting a requested status, directly.
- Use a model judge for more subjective criteria, such as extracting a useful title.
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Evaluation from usage and feedback
Jacob explains how Linear combines test cases and model judgment.
Linear engineer Jacob says the team grows its AI-agent evaluation dataset through actual use. When a user behaves in an unexpected way, the team adds that case for future testing. Some checks are objective: if a user asks for an issue to be in progress, the agent should set that status. Other questions, such as whether a description is well organized or the right information became the title, are more subjective. For those, he describes using a language model as a judge against the expected information.
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