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What is reranking in a RAG pipeline, and why does a cross-encoder reranker improve results?

Reranking is the cheapest large win in RAG quality: retrieve broadly, then reorder precisely. The signal is the bi-encoder-versus-cross-encoder distinction and the retrieve-then-rerank two-stage design. Here is the answer.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Reranking is the cheapest large win in RAG quality: retrieve broadly, then reorder precisely. The signal is the bi-encoder-versus-cross-encoder distinction and the retrieve-then-rerank two-stage design. Here is the answer.

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Next in this trackHow do you combine lexical (BM25) and semantic (vector) retrieval, and what is Reciprocal Rank Fusion?Next in this trackHow do you handle multi-hop questions in RAG (questions needing several pieces of evidence)?Next in this trackHow do you keep a RAG system's knowledge fresh (index updates, stale data, changing documents)?
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