Authors - Kamasani Vishnuvardhan Reddy, Anjan Babu G Abstract - Universities maintain extensive repositories of institutional knowledge, yet students struggle to extract accurate information from disparate sources such as PDF circulars, web portals, and notice boards. Rule-based chatbots handle only narrow query sets, while general-purpose large language models (LLMs) produce fluent but sometimes fabricated responses—a phenomenon termed hallucination. This paper presents the Intelligent Campus Assistant Chatbot for Sri Venkateswara University (SVU), employing a Retrieval-Augmented Generation (RAG) pipeline that grounds every response in verified institutional documents via dense semantic vector search and deterministic keyword retrieval fused through Reciprocal Rank Fusion (RRF). Evaluation on a 200-query benchmark yields 94.2% factual correctness, hallucination rates below 1%, mean latency of 0.8 s, and inter-rater agreement κ = 0.87 across English, Telugu, and Hindi.
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