Junior interview question
What is hybrid search?
Model answer
Combining term-based and embedding-based retrieval, because they fail differently.
Scroll sideways to see the whole diagram
In sequence: a cheap, less precise retriever fetches a candidate set. AI Engineering's example: every document containing “transformer”.
Text version
- In sequence: a cheap, less precise retriever fetches a candidate set. AI Engineering's example: every document containing “transformer”.
- A more precise, more expensive step orders the candidates: here, vector search picks the ones about the neural architecture. This second step is also called reranking.
- The catch: anything the first step didn't fetch is gone for good.
- In parallel: run several retrievers on the same query, each producing its own ranking.
- Combine the rankings into one, for example with reciprocal rank fusion. A document only one retriever found can still make the final list.
Either in sequence (a cheap retriever fetches candidates, a precise step orders them) or in parallel (both rank, then the rankings are fused).
Practise the rest
6 Hybrid & reranking questions, 492 in all, each with a model answer. Free.
More Hybrid & reranking interview questions
- How does reciprocal rank fusion work, and why use ranks rather than scores?
- When can fusion make retrieval worse?
- What's the difference between an embedding model and a reranker?
- How do you size the candidate pool for a reranker?
- A reranker fixes most of your misses but still prefers “safe for children over three” to “not safe for children under three”. What do you conclude?