Although large language models rely on parametric knowledge to achieve exceptional performance across various question-answering tasks, they still face challenges when addressing knowledge-based long-tail questions. Augmented generation techniques, such as chain-of-thought prompting and retrieval augmentation, can effectively enhance the ability of these models to answer long-tail questions. However, improving accuracy through augmented generation often results in significant latency within question-answering systems. This paper addresses the issue of “when and how to augment the input” by proposing an adaptive question routing framework. This framework employs a query router to select the most appropriate augmentation path at the right time, thereby enhancing both the accuracy and efficiency of question-answering systems. Extensive comparative experiments on benchmarks such as AmbigNQ, HotpotQA, MMLU-STEM, and PopQA demonstrate that our method surpasses existing approaches in both accuracy and efficiency. Furthermore, this paper introduces two metrics for evaluating adaptive question augmentation methods and presents a new benchmark for adaptive question augmentation, aiming to advance the field.