Reciprocal Rank Fusion (RRF)
An algorithm that combines ranked result lists from multiple retrieval methods into a single merged ranking without requiring score normalization.
RRF assigns each document a score of 1/(k + rank) for each retrieval method, where k is a smoothing constant (typically 60) and rank is the document's position in that method's result list. The per-method scores are summed across all methods, and documents are sorted by combined score. RRF is particularly valuable in hybrid retrieval because vector search scores and BM25 scores exist on incompatible scales — normalizing them requires calibration that may not generalize across query types.
RRF consistently outperforms score-based fusion in benchmark evaluations and is robust to the specific choice of retrieval methods being combined. In document intelligence systems, RRF is typically applied to merge vector search results (high semantic relevance) and BM25 results (high keyword precision), ensuring that documents ranking highly in either method are promoted in the final result set. This hybrid approach captures both the semantic understanding of dense retrieval and the exact-match reliability of sparse retrieval.
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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