What is model calibration, why does it matter, and how do you measure and fix it?
A model can rank perfectly and still output meaningless probabilities. The signal is knowing precisely when calibration matters, how to measure it, and the post-hoc fixes. Here is the answer most candidates miss.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A model can rank perfectly and still output meaningless probabilities. The signal is knowing precisely when calibration matters, how to measure it, and the post-hoc fixes. Here is the answer most candidates miss.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
