Talk · · Room 3.03
RAG Found It. Should Your AI Say It?
With Yuting Mao
AnuInfo is a chatbot built by a GDG on Campus ANU project team. It answers questions about ANU using official university sources and gives you the pages the answer came from. ANU information is spread across course pages, the events calendar, accommodation, scholarships, jobs and student support, each on its own site with its own layout. Searching them is painful, so students give up before they find the thing that helps them. That matters most for the services people need when something has gone wrong: booking the free legal service, finding a scholarship they are eligible for, working out who to contact for support. AnuInfo makes those reachable by asking a normal question. It is a RAG pipeline with agents on top. Retrieval matches your question against the content of the pages rather than titles and keywords, agents handle the things a single retrieval pass cannot, and the index is polled and refreshed so answers track changes on the source sites. What we cover: • The RAG pipeline: ingestion, chunking, embedding, retrieval, and how we keep answers grounded in real pages • How agents work with a RAG model • Keeping the index current as ANU pages change through the year • Getting the model to say it does not know instead of inventing a deadline • Running it publicly: prompt injection, staying on topic, rate limiting • Shipping it as a student team with mentors on a semester timeline . You leave with a practical picture of building RAG and agents on messy real-world web content, and what it takes to make it reliable and safe for use.
