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An eval harness found what qualitative review couldn't: AI models are most confident when wrong
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An eval harness found what qualitative review couldn't: AI models are most confident when wrong
There is a step in the development process for large language model (LLM)-assisted tooling that most teams skip because it's tedious, time-consuming, and doesn't produce results visible to end users: Verifying that what the model is saying is actually correct. Not fluent, not coherent, not topically relevant — correct in the sense of accurately identifying the right answer to the specific problem the tool was built to solve.The gap between "this…
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