Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering

• URL: http://arxiv.org/abs/2507.03405v1
• Date: Fri, 04 Jul 2025 09:13:50 GMT
• Title: Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering
• Authors: Krishna Ronanki, Simon Arvidsson, Johan Axell,
• Abstract summary: generative AI models like Large Language Models (LLMs) have demonstrated their utility across various activities, including within Requirements Engineering (RE)<n> Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses.<n>Existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities.
• Score: 2.867517731896504
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid emergence of generative AI models like Large Language Models (LLMs) has demonstrated its utility across various activities, including within Requirements Engineering (RE). Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses. However, existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities. The objective of this study is to explore the applicability of existing prompt engineering guidelines for the effective usage of LLMs within RE. To achieve this goal, we began by conducting a systematic review of primary literature to compile a non-exhaustive list of prompt engineering guidelines. Then, we conducted interviews with RE experts to present the extracted guidelines and gain insights on the advantages and limitations of their application within RE. Our literature review indicates a shortage of prompt engineering guidelines for domain-specific activities, specifically for RE. Our proposed mapping contributes to addressing this shortage. We conclude our study by identifying an important future line of research within this field.

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