# Requirements Engineering using Generative AI: Prompts and Prompting Patterns

arXiv: https://arxiv.org/abs/2311.03832 · HTML: https://mlsanity.com/paper/2311.03832/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2023.

## Built on by (most cited first, 13 of 13)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Model Generation with LLMs: From Requirements to UML Sequence Diagrams](https://mlsanity.com/paper/2404.06371/) (2024)
- [Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models](https://mlsanity.com/paper/2507.05981/) (2025)
- [Benchmarking and Studying the LLM-based Agent System in End-to-End Software Development](https://mlsanity.com/paper/2511.04064/) (2025)
- [On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software](https://mlsanity.com/paper/2504.02141/) (2025)
- [Reflexive Prompt Engineering: A Framework for Responsible Prompt Engineering and Interaction Design](https://mlsanity.com/paper/2504.16204/) (2025)
- [Vibe Coding in Practice: Flow, Technical Debt, and Guidelines for Sustainable Use](https://mlsanity.com/paper/2512.11922/) (2025)
- [How Effective are Generative Large Language Models in Performing Requirements Classification?](https://mlsanity.com/paper/2504.16768/) (2025)
- [Multi-Agent LLMs as Ethics Advocates for AI-Based Systems](https://mlsanity.com/paper/2507.08392/) (2025)
- [Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models](https://mlsanity.com/paper/2607.16708/) (2026)
- [Automated Alignment between Elicitation Interviews and Requirements](https://mlsanity.com/paper/2510.08622/) (2025)
- [ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation](https://mlsanity.com/paper/2604.23124/) (2026)
- [Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey](https://mlsanity.com/paper/2605.01392/) (2026)

## Abstract (arXiv)

[Context]: Companies are increasingly recognizing the importance of automating Requirements Engineering (RE) tasks due to their resource-intensive nature. The advent of GenAI has made these tasks more amenable to automation, thanks to its ability to understand and interpret context effectively. [Problem]: However, in the context of GenAI, prompt engineering is a critical factor for success. Despite this, we currently lack tools and methods to systematically assess and determine the most effective prompt patterns to employ for a particular RE task. [Method]: Two tasks related to requirements, specifically requirement classification and tracing, were automated using the GPT-3.5 turbo API. The performance evaluation involved assessing various prompts created using 5 prompt patterns and implemented programmatically to perform the selected RE tasks, focusing on metrics such as precision, recall, accuracy, and F-Score. [Results]: This paper evaluates the effectiveness of the 5 prompt patterns' ability to make GPT-3.5 turbo perform the selected RE tasks and offers recommendations on which prompt pattern to use for a specific RE task. Additionally, it also provides an evaluation framework as a reference for researchers and practitioners who want to evaluate different prompt patterns for different RE tasks.