In Depth
Reranking is a second-stage retrieval technique that takes an initial set of search results and re-scores them using a more sophisticated model to improve ordering. The initial retrieval (using keyword or semantic search) casts a wide net quickly, while the reranker uses a more computationally expensive cross-encoder model that considers the query and each document together to produce more accurate relevance scores.
The key difference between retrieval and reranking models is how they process queries and documents. Retrieval models (bi-encoders) encode queries and documents independently, enabling fast search across millions of documents. Reranking models (cross-encoders) process the query and document together, enabling deeper understanding of relevance but at higher computational cost. By using fast retrieval to get a candidate set of 50-100 documents and then reranking to find the top 5-10, systems achieve both speed and accuracy.
Reranking is a critical component of modern RAG pipelines and enterprise search systems. Models like Cohere Rerank, BGE Reranker, and cross-encoder models from Hugging Face are commonly used. The improvement from adding a reranking step is often dramatic, particularly for complex queries where initial retrieval returns a mix of relevant and irrelevant results. It is one of the highest-impact improvements teams can make to RAG system quality.