Carolina Guide: A Multi-Agent RAG System with Institutional Guardrails for Academic Policy Assistance
University students often struggle to navigate complex academic policies, leading to advising bottlenecks and delayed access to critical information. Although large language models (LLMs) offer promise for automated assistance, their tendency toward hallucination and inability to enforce institutional constraints make them unsuitable for high-stakes policy guidance without careful architectural design. We present Carolina Guide, a retrieval-augmented generation (RAG) system for academic policy assistance at the University of South Carolina (USC). The system employs a modular multi-agent pipeline with institutional guardrails to provide citation-supported, policy-grounded answers to studen...
| Authors | Ben Torsion, Jun Zhou |
|---|---|
| Primary category | cs.IR |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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