What is the difference between sparse and dense embeddings, and why does hybrid retrieval combine them?
Dense versus sparse is the core retrieval tradeoff, and 'just use embeddings' quietly loses every exact-match query. The signal is knowing what each representation captures and why production blends them. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Dense versus sparse is the core retrieval tradeoff, and 'just use embeddings' quietly loses every exact-match query. The signal is knowing what each representation captures and why production blends them. Here is the answer.
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.
