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A second-stage model that re-orders an initial set of retrieved candidates based on query-document relevance. Rerankers typically use cross-encoder architectures that consider query and document together, more accurate than the bi-encoder used for initial retrieval but too slow to apply across the full corpus.
Adding a reranker to a RAG pipeline often delivers the largest single-step quality improvement. AI engineers building production RAG ship rerankers as standard practice.
A second-stage model that re-orders an initial set of retrieved candidates based on query-document relevance. Rerankers typically use cross-encoder architectures that consider query and document together, more accurate than the bi-encoder used for initial retrieval but too slow to apply across the full corpus.
Adding a reranker to a RAG pipeline often delivers the largest single-step quality improvement. AI engineers building production RAG ship rerankers as standard practice.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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