Prompt engineering guidelines for LLMs in Requirements Engineering

Abstract

The rapid emergence of large generative AI models has demonstrated their utility across a multitude of tasks. Ensuring the quality and accuracy of the models’ output is done in different ways. In this study, we focused on prompt engineering. Prompt engineering guidelines for how to utilize large generative AI models in the field of requirements engineering are limited in the literature.The objective of this study was to explore the potential advantages and limitations of the possible application of existing prompt engineering guidelines from the literature in requirements engineering. To achieve this goal, we conducted a systematic literature review on prompt engineering guidelines to gather guidelines which could be applicable to various tasks. Subsequently, we considered different requirements engineering activities and their characteristics before proposing a mapping of our gathered guidelines to requirements engineering activities. Furthermore, we conducted interviews with three requirements engineering experts to gain further perspectives on our findings and mapping suggestions. Through thematic analysis, we extracted the advantages and limitations of the mapping. While our review shows how prompt guidelines for domain-specific tasks still are limited in literature, we did identify prompt guidelines in the current literature which show promise when working with an LLM in the practice of requirements specification. Additionally, we draw the conclusion that large generative AI models as we know them might not be fully ready for certain tasks in requirements engineering and suggest future work to explore how guidelines could be adapted to fit other requirements engineering tasks better.