In university systems, traditional methods of information retrieval are often found to be inefficient, leading to frustration among students and staff. This paper presents the development and evaluation of a university-specific chatbot that employs the Retrieval-Augmented Generation (RAG) approach to improve the accuracy and relevance of its responses. Unlike conventional chatbots that depend on intent classification and pre-designed system responses and conversation flows, the proposed chatbot integrates Large Language Models (LLMs) with local university data, enhancing its ability to handle complex queries with context-aware responses and dynamically generated conversation flows. The system architecture includes components such as LangChain for orchestration, a vector store for embedding external knowledge, and a user interface developed using Streamlit. Evaluation results demonstrate that the RAG-based chatbot substantially outperforms traditional LLMs, including GPT-3.5, GPT-4 mini, and GPT-4, in terms of answer accuracy and reliability. In this paper we also reflect on the lessons learned during the chatbot’s development and deployment in a real-world university setting.