# Reciprocal rank fusion: why combining search results is harder than it looks

DevFeed: [Reciprocal rank fusion: why combining search results is harder than it looks](<https://devfeed.tech/articles/reciprocal-rank-fusion-why-combining-search-results-is-harder-than-it-looks-4847.md>)

Original publisher: [Read original article](<https://redis.io/blog/reciprocal-rank-fusion/>)

Author: Jeff Mills

Published: 2026-08-09T00:00:00Z

Content type: tutorial

Language: en

Sources: [Redis Blog](<https://devfeed.tech/sources/redis-blog.md>)

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [Redis](<https://devfeed.tech/topics/redis.md>), [real-time](<https://devfeed.tech/topics/real-time.md>)

Tags: [guide](<https://devfeed.tech/tags/guide.md>), [redis](<https://devfeed.tech/tags/redis.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [search](<https://devfeed.tech/tags/search.md>), [tech-de](<https://devfeed.tech/tags/tech-de.md>), [vector](<https://devfeed.tech/tags/vector.md>)

## AI overview

A guide to reciprocal rank fusion (RRF), a method for combining keyword and vector search rankings without directly adding their raw scores. It explains the formula, the role of the constant k, why agreement between retrievers matters, and RRF's unsupervised nature.

## Source excerpt

You run a keyword search and get back a ranked list with Best Matching 25 (BM25) scores. You run a vector search over the same documents and get a second list with cosine similarities. You want to merge them into a single ranking that surfaces the mos...