Why can't you evaluate an LLM application the way you evaluate a classifier?
Accuracy against a held-out label is the wrong instrument for a system with no single right answer, an open failure space, and a metric that is itself a model. Here is what breaks, what survives, and what replaces it.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Accuracy against a held-out label is the wrong instrument for a system with no single right answer, an open failure space, and a metric that is itself a model. Here is what breaks, what survives, and what replaces it.
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.
