Creating and deploying customized applications is crucial for operational success and enriching user experiences in the rapidly evolving modern business world. A prominent facet of modern user experiences is the integration of chatbots or voice assistants. The rapid evolution of Large Language Models (LLMs) has provided a powerful tool to build conversational applications. We present Walert, a customized LLM-based conversational agent able to answer frequently asked questions about computer science degrees and programs at RMIT University. Our demo aims to showcase how conversational information-seeking researchers can effectively communicate the benefits of using best practices to stakeholders interested in developing and deploying LLM-based chatbots. These practices are well-known in our community but often overlooked by practitioners who may not have access to this knowledge. The methodology and resources used in this demo serve as a bridge to facilitate knowledge transfer from experts, address industry professionals' practical needs, and foster a collaborative environment. The data and code of the demo are available at https://github.com/rmit-ir/walert.

翻译：在现代商业环境快速演变的背景下，创建和部署定制化应用对于运营成功及提升用户体验至关重要。现代用户体验的显著特征之一是聊天机器人或语音助手的集成。大型语言模型（LLMs）的快速发展为我们构建对话应用提供了强大工具。本文展示Walert——一个基于定制化LLM的对话代理，能够回答关于RMIT大学计算机科学学位及项目的常见问题。本演示旨在展示对话式信息检索研究人员如何有效地向对开发与部署LLM聊天机器人感兴趣的 stakeholders 传达应用最佳实践的益处。这些实践在学术界广为人知，但往往被未能接触相关知识的从业者所忽视。本演示所使用的方法与资源搭建了一座桥梁，促进专家知识向产业专业人士实际需求的转化，并营造协作环境。演示的数据与代码已开源至 https://github.com/rmit-ir/walert。