Your LLM app aces the eval set and is brittle in the wild. How do you test robustness to input variation?
Your eval set is clean, well-punctuated, and phrased the way you think about the problem. Your users are not. The move that turns this from a vibe check into an engineering artifact is measuring invariance rather than accuracy, and gating on the number it produces.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Your eval set is clean, well-punctuated, and phrased the way you think about the problem. Your users are not. The move that turns this from a vibe check into an engineering artifact is measuring invariance rather than accuracy, and gating on the number it produces.
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.
