How do you detect out-of-distribution inputs, and why does it matter for safe deployment?
Models return confident answers on inputs unlike anything in their training set, which is how silent production failures start. The signal is knowing why raw softmax confidence misleads and which detectors genuinely separate in- from out-of-distribution.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Models return confident answers on inputs unlike anything in their training set, which is how silent production failures start. The signal is knowing why raw softmax confidence misleads and which detectors genuinely separate in- from out-of-distribution.
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.
