The enhanced context for AI-generated learning advisors with Advanced RAG

In Van Hien University, which serves tens of thousands of students, efficiently addressing student inquiries related to training programs, tuition fees, graduation conditions, and output standards has become a critical and urgent challenge. Traditional advisory methods are increasingly strained by the volume of queries and the complexity of providing accurate, context-specific guidance. This research introduces an AI Agent leveraging the Advanced Retrieval-Augmented Generation (RAG) model to solve this problem. The AI Agent is designed to dynamically retrieve relevant information from extensive institutional documentation and provide precise, context-aware advice to students. By integrating cutting-edge language models with a robust retrieval mechanism, the proposed system aims to enhance the accuracy, relevance, and scalability of student advisory services. This approach not only improves the efficiency of responding to student needs but also ensures that the guidance provided aligns closely with the university’s standards and policies, ultimately contributing to a more streamlined and effective student support system.