IUB-RAG-LLM: an extensible system for continuous knowledge ingestion and asynchronous indexing

Abstract

Disseminating institutional knowledge at universities is challenging, resulting in information gaps for students and increased burdens on both faculty and staff who must answer repetitive queries. We present IUB-RAG-LLM, an extensible retrieval-augmented generation system designed to provide instant access to institutional knowledge. The system features an asynchronous data pipeline that reduces knowledge update latency by 6x, and a modular architecture that enables integration of new data sources and long-term maintainability. We created a corpus of 2834 documents produced from official university channels, which is processed into 7798 semantic chunks and continuously updated. A preliminary human evaluation study with 3 student participants assessing 60 question-answer pairs demonstrated the system’s effectiveness, with average correctness and completeness scores of 3.57/5.00 and 3.67/5.00, respectively. The IUB-RAG-LLM system shows promise in alleviating faculty and staff workload while improving student access to accurate, timely institutional information. Our project can be found at https://github.com/znnahiyan/iub-rag-llm.