What are hybrid search and reranking, and when does a RAG system actually need them?
What they are really testing: The standard second-generation retrieval stack. This checks you know what each stage fixes, exact-match blindness and precision-at-top respectively, rather than cargo-culting a pipeline diagram.
A real interview question
What are hybrid search and reranking, and when does a RAG system actually need them?
What most people say
drag me
“Hybrid search combines keyword and semantic search for better results, and reranking reorders results with a better model, both are best practices for RAG.”
Both definitions are right and the reasoning is absent. "Best practice" is the tell: each stage adds latency, cost and operational surface, and the decision to add them should come from a measured retrieval failure, not a diagram.
The follow-ups they ask next
Why is a cross-encoder more accurate than embedding similarity at all?
Bi-encoders compress query and document into vectors independently, then compare. A cross-encoder attends across both texts jointly, so it can match specifics that compression lost.
Your reranker added 150ms and users notice. Options?
Rerank fewer candidates, use a lighter reranker, run it only for query types that need it, or overlap it with other pipeline stages. Or revisit whether precision at 5 was really the bottleneck.
What the interviewer is listening for
- Maps hybrid to exact-match blindness with concrete examples
- Knows the retrieve-wide-then-rerank-narrow pattern and its latency price
- Adds stages only when a retrieval eval shows the specific failure
What sinks the answer
- Recommends the full stack everywhere as best practice
- Cannot say what reranking fixes that retrieval missed
- No measurement distinguishing recall from precision problems
If you genuinely do not know
Say this instead of freezing. Reasoning out loud from what you do know beats silence every single time, and a good interviewer is listening for exactly that.
“Hybrid fixes [dense retrieval missing exact codes and names] by [fusing BM25 with vector search]. Reranking fixes [mediocre ordering in the top candidates] by [cross-encoding query and document together, retrieve 50, rerank to 5]. Add each [when a retrieval eval shows its failure], not by default.”
Keep going with rag & retrieval
Foundation
What is an embedding, and what does "similar" actually mean when you search with one?
Foundation
Explain RAG to me, and tell me what problem it solves that a bigger model does not.
Foundation
Do you actually need a dedicated vector database to ship a RAG feature? Walk me through how you would decide.
Junior
How do you decide how to chunk documents for a RAG system, and what goes wrong with naive chunking?
Mid
Users report your RAG assistant is giving wrong answers. Walk me through how you debug it.
Mid
What actually is an agent, mechanically, and what stops one from looping forever or going off the rails?
Knowing the answer is not the same as recalling it under pressure
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