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

Just Tell Me: Prompt Engineering in Business Process Management