FastAPI

RAG-based multimodal university chatbot reduces hallucination from 31.7% to 6.6%

Researchers present a multimodal university chatbot system combining LLMs with retrieval-augmented generation to answer text and image queries from institutional documents like university handbooks. The system uses a vision-language model, quantized inference for constrained hardware, and a FastAPI/Next.js stack. Evaluation shows hallucination reduction from 31.7% to 6.6% compared to a non-RAG baseline, with strong satisfaction scores across modalities.