  This paper presents BARKPLUG V.2, a Large Language Model (LLM)-based chatbot
system built using Retrieval Augmented Generation (RAG) pipelines to enhance
the user experience and access to information within academic settings.The
objective of BARKPLUG V.2 is to provide information to users about various
campus resources, including academic departments, programs, campus facilities,
and student resources at a university setting in an interactive fashion. Our
system leverages university data as an external data corpus and ingests it into
our RAG pipelines for domain-specific question-answering tasks. We evaluate the
effectiveness of our system in generating accurate and pertinent responses for
Mississippi State University, as a case study, using quantitative measures,
employing frameworks such as Retrieval Augmented Generation Assessment(RAGAS).
Furthermore, we evaluate the usability of this system via subjective
satisfaction surveys using the System Usability Scale (SUS). Our system
demonstrates impressive quantitative performance, with a mean RAGAS score of
0.96, and experience, as validated by usability assessments.
