ProFILL, a method for transforming hoBIT, the authors' college's current rule-based advising chatbot, into a profile-aware retrieval-augmented generation (RAG) system, is presented, which outperforms diverse RAG baselines, is preferred by target users, and remains effective with open-weight models for cost-effective on-premise deployment.
Abstract
In university academic advising, identical questions can require different answers depending on a student's department, admission cohort, and degree program, causing profile-blind retrievers to surface plausible but inapplicable evidence. We present proFILL, a method for transforming hoBIT, our college's current rule-based advising chatbot, into a profile-aware retrieval-augmented generation (RAG) system. Rather than requiring a complete user profile upfront, proFILL progressively acquires only the profile attributes needed for each query, guided by both the query intent and the initially retrieved evidence, and uses them to condition retrieval over a profile-aware index. Extensive experiments and a human preference study show that proFILL outperforms diverse RAG baselines, is preferred by target users, and remains effective with open-weight models for cost-effective on-premise deployment.
This project presents an AI-Powered College Enquiry Chatbot that automates common college-related queries using Retrieval-Augmented Generation (RAG) and provides an administrative dashboard for managing FAQ content and monitoring chatbot performance.
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