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&#x27;s tedious, time-consuming, and doesn&#x27;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…