A specialist that does one thing well: deciding which documents actually matter for a given query. Unlike embedding models that encode text independently, it reads a query and document together to score their relevance — making it slower but more precise. It shines in retrieval pipelines as a second-pass filter, catching relevance signals that vector similarity search tends to miss.