We propose a simple yet a novel approach to improve completion in domain modeling activities. Our approach exploits the power of large language models by using few-shot prompt learning without the need to train or fine-tune those models with large datasets that are scarce in this field. We implemented our approach and tested it on the completion of static and dynamic domain diagrams. Our initial evaluation shows that such an approach is effective and can be integrated in different ways during the modeling activities.

Chatbots已经彻底改变了人类与计算机系统互动的方式，他们替代了服务代理，呼叫中心代表等。健身行业一直是一种不断增长的行业，尽管它还没有适应AI，ML和云计算等最新技术。在本文中，我们建议使用IBM Watson开发适合健身管理的聊天栏，并将其与Web应用程序集成。我们建议使用自然语言处理（NLP）和自然语言理解（NLU）以及为Chatbot Assistant提供的IBM Cloud Watson框架。该软件采用无服务器架构，通过提供饮食计划，家庭练习，互动咨询会，健身建议，将专业的服务结合起来。