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Rerank is the fine-ranking stage after multi-way retrieval in RAG, used to more finely score the relevance of candidate documents to the question.
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10/15/2026
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Unverified文本分块策略、检索召回数量(Top-K)、重排序模型(Reranker)是影响RAG系统最终效果的三个关键变量77% similarVerifiedReranking technology first uses coarse retrieval to recall a large number of candidate segments, then uses a fine-ranking model to re-score them, keeping only the most relevant content to feed into the model.75% similarUnverified在RAG流程返回大量候选文档片段时,可先按相关性排序再截取前3-5项传入大模型以控制输入长度74% similarUnverified召回加重排(Recall + Rerank)先用 embedding 或关键词检索召回 Top 10 候选,再交给大模型做最终裁决,与RAG思路一致73% similarVerifiedCRAG adds an evaluation-and-correction mechanism on top of standard RAG: it first scores the relevance of retrieved documents, and if the score falls below a threshold, it triggers corrective steps such as web search or query rewriting.72% similar
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