This paper presents a pilot study of a university virtual support agent system powered by a Large Language Model (LLM) engineered to address student inquiries regarding processes and policies involving a student exchange program. Traditional approaches to implementing chatbots in university student support services have had their shortcomings. This study investigates the enablement of conversational support using generative artificial intelligence and natural conversation properties inherent in LLMs and how they may overcome such shortcomings from earlier attempts at conversational support. The LLMs’ susceptibility to ‘hallucination’ is mitigated through a combined approach of few-shot learning, retrieval augmented generation (RAG), and chain of thought (CoT) libraries in the training phase. The system utilizes OpenAI’s GPT-4o model behind a Python-based web framework, enabling the conversational interface used by the students. The information system is grounded in earlier email correspondences from program heads to students, PDF files containing primers, and other sources. The pilot implementation of the system involves an outbound student exchange program in a private university in the Philippines. Sophomore students were invited to test and evaluate the system and interviewed after the chatbot sessions.