  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.
