  GPT-3 and several other language models (LMs) can effectively address various
natural language processing (NLP) tasks, including machine translation and text
summarization. Recently, they have also been successfully employed in the
business process management (BPM) domain, e.g., for predictive process
monitoring and process extraction from text. This, however, typically requires
fine-tuning the employed LM, which, among others, necessitates large amounts of
suitable training data. A possible solution to this problem is the use of
prompt engineering, which leverages pre-trained LMs without fine-tuning them.
Recognizing this, we argue that prompt engineering can help bring the
capabilities of LMs to BPM research. We use this position paper to develop a
research agenda for the use of prompt engineering for BPM research by
identifying the associated potentials and challenges.
