Students often report difficulties in accessing day-to-day academic information, which is usually spread across numerous institutional documents and websites. This fragmentation results in a lack of clarity and causes confusion about routine university information. This project proposes the development of a chatbot using Generative Artificial Intelligence (GenAI) and Retrieval-Augmented Generation (RAG) to simplify access to such information. Several GenAI models were tested and evaluated based on quality metrics and the LLM-as-a-Judge approach. Among them, Gemini 2.0 Flash stood out for its quality and speed, and Gemma 3n for its good performance and open-source nature.
翻译：学生常常反映难以获取日常学术信息，这些信息通常分散在众多机构文件和网站中。这种碎片化导致信息缺乏清晰度，并引发对常规大学信息的困惑。本项目提出开发一款利用生成式人工智能（GenAI）和检索增强生成（RAG）技术的聊天机器人，以简化此类信息的获取。基于质量指标和LLM-as-a-Judge方法，我们对多个GenAI模型进行了测试和评估。其中，Gemini 2.0 Flash因其质量和速度表现突出，而Gemma 3n则因其良好性能和开源特性而受到关注。
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