Reranking enhances RAG quality by refining initial document retrieval

Retrieval-augmented generation (RAG) systems can significantly improve answer quality by implementing a reranking step after initial retrieval. This process uses a fast bi-encoder to retrieve a broad set of candidate documents, followed by a slower but more accurate cross-encoder that re-evaluates the relevance of a smaller subset of documents by considering the query and document together. This "retrieve wide, rerank narrow" approach allows the best document to be identified and presented to the LLM, enhancing the final output. AI

IMPACT Improves the accuracy and relevance of answers generated by LLM applications using RAG.

RANK_REASON Describes a technique for improving existing AI systems, not a new release or core research.

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• PromptFromZero
• retrieval-augmented generation

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