Two-Stage Retrieval in Enterprise Search: How Rerankers Improve AI

Rerankers play a crucial role in enterprise search by refining the results of basic retrieval systems, significantly improving search relevance without requiring a full system rebuild. They operate as part of a two-stage retrieval process, first performing similarity search and then re-evaluating the retrieved documents to assign higher scores to the most contextually relevant results. There are three primary categories of rerankers: lightweight rescoring methods, bi-encoders, and cross-encoders, each with its strengths and weaknesses in terms of speed, interpretability, and accuracy. By adding a reranker layer to an AI search pipeline, enterprises can improve search accuracy by over 10%, reduce hallucinations, and enhance user experience through smarter recommendations.

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