Pick the wrong distance metric and you quietly break kNN, clustering, and retrieval. What matters is knowing what each metric actually measures and matching it to the data: magnitude vs direction, sets, correlated features.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Pick the wrong distance metric and you quietly break kNN, clustering, and retrieval. What matters is knowing what each metric actually measures and matching it to the data: magnitude vs direction, sets, correlated features.
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.
