Authors: Ms. K. Sabitha, B. Monish, M. Nithishkumar, S. Mohammed Al Ameen, S. Samvarthini
Abstract: The process of selecting an appropriate engineering course and college has become increasingly challenging due to the large volume of information and the complexity of admission procedures such as TNEA. Students often face difficulty in understanding cutoff trends, identifying suitable colleges, and making informed decisions because the available information is scattered and sometimes unreliable. To address this issue, this project proposes an intelligent academic assistance system that combines Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs). The system is designed to provide accurate and user-friendly guidance by retrieving verified academic data and presenting it through an interactive conversational interface. The retrieval component ensures that information such as cutoff marks and college details is obtained from structured datasets, while the language model supports explanation-based queries related to courses and career paths. The system is implemented as a web-based application using modern technologies, enabling real-time interaction between the user and the system. By combining data retrieval techniques with intelligent response generation, the proposed solution improves accuracy, reduces misinformation, and enhances user experience. This approach simplifies the decision-making process and helps students choose suitable academic paths with confidence.
