Reciprocal Rank Fusion explained: how hybrid search merges retriever results

Hybrid search systems use multiple retrievers — such as keyword-based BM25 and dense embedding models — each returning their own ranked list of documents for a query. Merging these lists by averaging raw scores fails because retrievers operate on vastly different numerical scales, causing the one with larger numbers to dominate the result. Reciprocal Rank Fusion (RRF) solves this by discarding raw scores entirely and instead converting each document's rank position into a value of 1/(60 + rank), then summing these values across all retrievers. The constant 60 dampens the gap between top-ranked and lower-ranked documents, ensuring a document ranked highly by multiple retrievers rises to the top of the final merged list. However, RRF has tradeoffs and is not always the right tool, particularly in scenarios where the magnitude of scores carries meaningful signal that rank position alone cannot capture.