This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) to enhance information retrieval and query response systems for university-related question answering. By systematically extracting data from the university official webpage and employing advanced prompt engineering techniques, we generate accurate, contextually relevant responses to user queries. We developed a comprehensive university benchmark, UniversityQuestionBench (UQB), to rigorously evaluate our system performance, based on common key metrics in the filed of RAG pipelines, assessing accuracy and reliability through various metrics and real-world scenarios. Our experimental results demonstrate significant improvements in the precision and relevance of generated responses, enhancing user experience and reducing the time required to obtain relevant answers. In summary, this paper presents a novel application of RAG pipelines and LLMs, supported by a meticulously prepared university benchmark, offering valuable insights into advanced AI techniques for academic data retrieval and setting the stage for future research in this domain.

翻译：本文提出了一种创新方法，利用检索增强生成（RAG）流程与大语言模型（LLM）相结合，以增强面向大学相关问答的信息检索与查询响应系统。通过系统性地从大学官方网页提取数据，并采用先进的提示工程技术，我们能够针对用户查询生成准确且上下文相关的回答。我们开发了一个全面的大学基准测试集UniversityQuestionBench（UQB），以严格评估系统性能。该基准基于RAG流程领域的常见关键指标，通过多种度量标准和真实场景来评估准确性与可靠性。实验结果表明，所生成回答的精确度和相关性得到显著提升，从而改善了用户体验并缩短了获取相关答案所需的时间。总之，本文提出了一种RAG流程与LLM的新颖应用，并辅以一个精心构建的大学基准测试集，为学术数据检索的先进人工智能技术提供了有价值的见解，并为该领域的未来研究奠定了基础。