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Creating and deploying customized applications is crucial for operationalsuccess and enriching user experiences in the rapidly evolving modern businessworld. A prominent facet of modern user experiences is the integration ofchatbots or voice assistants. The rapid evolution of Large Language Models(LLMs) has provided a powerful tool to build conversational applications. Wepresent Walert, a customized LLM-based conversational agent able to answerfrequently asked questions about computer science degrees and programs at RMITUniversity. Our demo aims to showcase how conversational information-seekingresearchers can effectively communicate the benefits of using best practices tostakeholders interested in developing and deploying LLM-based chatbots. Thesepractices are well-known in our community but often overlooked by practitionerswho may not have access to this knowledge. The methodology and resources usedin this demo serve as a bridge to facilitate knowledge transfer from experts,address industry professionals' practical needs, and foster a collaborativeenvironment. The data and code of the demo are available athttps://github.com/rmit-ir/walert.

Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot