AI-Based College Information Assistant Using Rag

Students often need to search across college websites, admission brochures, circulars, academic calendars, fee notices, anddepartmental documents to find reliable information. This paper proposes an AI-based College Information Assistant usingRetrieval-Augmented Generation (RAG), a method that combines information retrieval with language generation. The proposedsystem accepts a natural-language question, retrieves relevant passages from approved college documents, and uses thosepassages to generate a concise response with source references. The workflow includes document ingestion, text cleaning,chunking, embedding generation, semantic retrieval, grounded answer generation, and safe fallback handling when evidence ismissing or conflicting. The design emphasizes traceability, document freshness, privacy, and avoidance of unsupported claims. Asuggested evaluation plan measures retrieval relevance, answer correctness, citation support, abstention quality, response time,and usability. The paper describes the problem, related work, architecture, methodology, implementation options, test scenarios,limitations, and future scope. As no implemented prototype or measured dataset results were supplied, the paper does not claimnumerical performance; these must be added after actual testing