hybrid search

Cosine similarity finds topically close, not correct. The failure taxonomy behind most RAG accuracy complaints, and the hybrid-search + reranker fix stack interviewers expect you to know cold.

Pure vector search fails on the exact queries enterprise users ask most: IDs, error codes, product names. Here's the failure-mode framing that turns a definition question into a design answer.

The retrieval design question with a precise rubric: candidates who can say what each stage rescues, and what it costs in latency, clear it. Includes the RRF detail and the latency budget interviewers ask for.

A deceptively short interview question that's really a trap: embeddings cannot do exact match, and the interviewer knows it. What to actually turn on (payload full-text indexes, sparse vectors, metadata filters) in the vector DBs you already run.