"When the model should say 'I don't know': confidence gates and human-in-the-loop design"
Models are trained test-takers: a wrong guess and "I don't know" score the same, so they guess. If a confident wrong answer costs you money or trust, abstention has to be engineered in — real confidence signals, a measured threshold, and a review queue that actually gets worked.
If an AI system has ever burned you, it probably wasn't because the model refused to answer. It answered — fluently, confidently, in perfect grammar — and it was wrong. The invoice total off by a digit, the customer detail that was invented, the classification that sent the wrong email to the wrong person. The damage wasn't the error itself; it was that nothing in the system's tone gave you any reason to doubt it.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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