Your model is accurate on average but fails badly for one subgroup. How do you find and fix it?
A 92% aggregate accuracy can mask 60% on the segment that matters most. Averages are exactly where these failures stay buried. Here is how to surface them and the menu of fixes that actually map to the cause.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A 92% aggregate accuracy can mask 60% on the segment that matters most. Averages are exactly where these failures stay buried. Here is how to surface them and the menu of fixes that actually map to the cause.
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.
