Have you ever asked a question and received a less-than-helpful answer? It turns out, even with the vast knowledge of Large Language Models (LLMs), vague questions can stump AI. Researchers have developed a clever solution called a "question rewriter" that acts like an interpreter between humans and LLMs. This rewriter takes unclear questions and polishes them into a format that AI can understand better, leading to more accurate answers. The magic of this approach lies in how the rewriter learns to improve questions. Instead of relying on costly human feedback, it uses automatic scoring methods that already exist within many question-answering datasets. By comparing the quality of answers to original and rewritten questions, the rewriter learns to create clearer, more concise questions without any human intervention. Tests across various LLMs and datasets show this method consistently improves answer quality. Surprisingly, analysis suggests the rewriter learns to create questions in a professional, unbiased, and concise style—exactly what we might intuitively consider a "good" question. This technology has significant implications for the future of question answering. Imagine search engines that understand the intent behind your vague queries or customer service bots that offer helpful solutions even when you're not sure how to phrase the problem. While this research focuses on specific domains, future work may explore how to create a universal question rewriter that works across various topics, opening exciting possibilities for more effective communication between humans and AI.