A focused retrieval specialist trained specifically for question-answer matching. It excels at understanding the semantic relationship between a question and a passage that might answer it, making it more precise in asymmetric search tasks than general-purpose embedding models. Works with cosine similarity, so vectors should be normalized before comparison.