How do you operate a multi-vector (ColBERT-style) index in production without it blowing up storage?
Late interaction keeps one vector per token, so a corpus that fit in a few GB as single vectors can swell 100x. Interviewers want to see you know the compression and indexing tricks (centroids, residuals, PLAID) that make multi-vector retrieval shippable.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Late interaction keeps one vector per token, so a corpus that fit in a few GB as single vectors can swell 100x. Interviewers want to see you know the compression and indexing tricks (centroids, residuals, PLAID) that make multi-vector retrieval shippable.
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.
