University regulations are often complex and difficult to navigate. To address this, we developed PoliChat, a Retrieval-Augmented Generation (RAG)-based chatbot that provides accurate and transparent access to regulatory information. Validated at Wrocław University of Science and Technology, PoliChat integrates real-time retrieval with citation mechanisms to enhance reliability. As part of our research, we prepared and annotated a dataset of university regulations to evaluate information retrieval and answer generation performance. We examine key factors that affect RAG performance in regulatory domains, including model size, document length, summarization, retrieved context size, and prompting strategies. We introduce Analyze&Answer, a prompting method that improves response coherence and citation accuracy.