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.

本文介绍了BARKPLUG V.2，一个基于大型语言模型（LLM）的聊天机器人系统，利用检索增强生成（RAG）流程提高学术环境下的用户体验和信息获取。该系统利用大学数据作为外部数据语料库，在领域特定的问答任务中吸收这些数据到我们的RAG流程中。我们使用定量测量方法，如检索增强生成评估（RAGAS）框架来评估我们的系统在密西西比州立大学作为案例研究时生成准确和相关回答的效果，并通过主观满意度调查，使用系统可用性量表（SUS）评估该系统的可用性。我们的系统在定量性能方面表现出色，平均RAGAS得分为0.96，并通过可用性评估得到验证。

从问题到有见地的回答: 建立一个了解大学资源的聊天机器人