Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering

2025-07-04

AI Summary

Existing prompt engineering guidelines lack domain-specific adaptation to requirements engineering (RE), limiting the effectiveness of large language models (LLMs) in RE tasks. Method: We conducted a systematic literature review and in-depth interviews with 12 RE domain experts to analyze the applicability and limitations of general-purpose prompt engineering methods across core RE activities—including requirements elicitation, analysis, and modeling. Contribution/Results: We propose PE-RE, the first prompt engineering mapping framework tailored to RE, which explicitly links prompt design principles to RE tasks, artifacts, and quality attributes. PE-RE bridges the gap in domain-specific prompt guidance, enabling more interpretable and controllable LLM deployment in RE. It also identifies concrete future research directions—namely, dynamic prompt optimization and the development of standardized evaluation benchmarks for RE-oriented prompting.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Knowledge Representation and Reasoning: Knowledge Engineering
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Lack of prompt engineering guidelines for Requirements Engineering
• Need to ensure quality of LLM-generated outputs in RE
• Exploring applicability of existing guidelines for RE tasks

Innovation

• Systematic review of prompt engineering guidelines
• Interviews with RE experts for insights
• Mapping guidelines for RE-specific activities