Researchers introduce DRAG, a query-adaptive framework for selecting retriever-generator configurations in Retrieval-Augmented Generation (RAG) systems, leading to improved effectiveness-efficiency trade-off. The framework includes two approaches: DRAG_QPP, a training-free routing method using query performance prediction, and DRAG_SFT, a supervised routing method that fine-tunes a large language model to predict retriever-generator configurations.