Comment by Buoylog

This matches what I've seen building anything that uses an LLM for narrow structured output rather than open ended chat, things like classifying a diff into a fixed set of categories or summarizing a change. The aggregate benchmark score barely predicts how it behaves in production. What actually breaks a pipeline is a confident wrong answer on the small slice of inputs that don't fit the pattern it saw during training, not a lack of raw capability. A model that says it isn't sure on the edge cases is far more useful to me than one that scores higher on average but never admits uncertainty, because the wrong but confident output is the one that slips through review unnoticed.