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Browse 3 real-world technical and behavioral interview questions about Reranking. Review scenarios, edge cases, and architectural best practices.
Debug a RAG system with confident wrong answers by separating retrieval failure from generation failure. Check whether the gold passage was retrieved, then measure recall@k, reranking quality, faithfulness and abstention on a labelled eval set. It also connects retrieval evaluation to the point an interviewer is testing.
Read the failing queries first and sort them into causes, then apply the matching lever — rewriting for vague queries, filters for scope errors, smaller retrieval units for buried answers, lexical retrieval for literal terms, a reranker where recall sits below the cut — each with the metric that proves it worked.
It earns it when your gold set shows recall at 50 is much better than recall at 5, because that gap is what reranking converts into answer quality. The cost is a model pass per candidate with nothing precomputable, so the latency budget divided by measured per-batch cost sets the candidate count.