How Does a RAG Reranker Really Work?

A reranker improves search relevance by processing a query and a document passage together, unlike standard embedders that analyze them separately. This joint analysis allows the model to learn which query words are statistically associated with answer words based on massive training datasets. This is especially powerful for finding correct answers that don't repeat the question's exact phrasing, a common failure point for simpler retrieval methods. However, this learned association breaks down with specialized vocabularies not seen during training, offering little advantage in domain-specific contexts like legal or medical documents.