REprompt: Prompt Generation for Intelligent Software Development Guided by Requirements Engineering

2026-01-23

AI Summary

This work proposes REprompt, a requirements engineering–driven multi-agent framework for prompt optimization that systematically integrates requirements analysis into the prompt generation pipeline. Existing automated prompt generation methods often neglect foundational principles of requirements engineering, resulting in prompts that fail to meet the rigor and specificity required in real-world software development. REprompt addresses this gap by leveraging multi-agent collaboration to automatically generate and refine both system and user prompts grounded in formal requirement specifications. Empirical evaluation demonstrates that REprompt significantly enhances the behavioral accuracy and requirements consistency of large language model outputs, effectively bridging the divide between automated prompt engineering and established software engineering practices.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

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

Abstract

The rapid development of large language models is transforming software development. Beyond serving as code auto-completion tools in integrated development environments, large language models increasingly function as foundation models within coding agents in vibe-coding scenarios. In such settings, prompts play a central role in agent-based intelligent software development, as they not only guide the behavior of large language models but also serve as carriers of user requirements. Under the dominant conversational paradigm, prompts are typically divided into system prompts and user prompts. System prompts provide high-level instructions to steer model behavior and establish conversational context, while user prompts represent inputs and requirements provided by human users. Despite their importance, designing effective prompts remains challenging, as it requires expertise in both prompt engineering and software engineering, particularly requirements engineering. To reduce the burden of manual prompt construction, numerous automated prompt engineering methods have been proposed. However, most existing approaches neglect the methodological principles of requirements engineering, limiting their ability to generate artifacts that conform to formal requirement specifications in realistic software development scenarios. To address this gap, we propose REprompt, a multi-agent prompt optimization framework guided by requirements engineering. Experiment results demonstrate that REprompt effectively optimizes both system and user prompts by grounding prompt generation in requirements engineering principles.

Problem

• prompt generation
• requirements engineering
• intelligent software development
• large language models
• prompt engineering

Innovation

• requirements engineering
• prompt generation
• large language models
• intelligent software development
• multi-agent framework