vector search

Retrieval quality decides most RAG products, and embeddings are where it starts. The interviewer wants the intuition, the failure modes, and the fixes a PM would push for.

Shorter and binary embeddings cut storage by orders of magnitude and lose a few points of retrieval. Decide with recall on your own queries and a rescoring step.

Cropping beats a bigger model, a smaller embedding buys back latency, and a stale index sends shoppers to items they cannot buy. Make each of those calls with numbers.