# Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap

arXiv: https://arxiv.org/abs/2507.07682 · HTML: https://mlsanity.com/paper/2507.07682/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2025.

## Built on by (most cited first, 2 of 2)

- [Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering](https://mlsanity.com/paper/2606.27960/) (2026)
- [Specifying the Delegated-Autonomy Boundary: Requirements Engineering for Agentic AI](https://mlsanity.com/paper/2607.17225/) (2026)

## Abstract (arXiv)

Advancements in large language models (LLMs) have led to a surge of prompt engineering (PE) techniques that can enhance various requirements engineering (RE) tasks. However, current LLMs are often characterized by significant uncertainty and a lack of controllability. This absence of clear guidance on how to effectively prompt LLMs acts as a barrier to their trustworthy implementation in the RE field. We present the first roadmap-oriented systematic literature review of Prompt Engineering for RE (PE4RE). Following Kitchenham's and Petersen's secondary-study protocol, we searched six digital libraries, screened 867 records, and analyzed 35 primary studies. To bring order to a fragmented landscape, we propose a hybrid taxonomy that links technique-oriented patterns (e.g., few-shot, Chain-of-Thought) to task-oriented RE roles (elicitation, validation, traceability). Two research questions, with five sub-questions, map the tasks addressed, LLM families used, and prompt types adopted, and expose current limitations and research gaps. Finally, we outline a step-by-step roadmap showing how today's ad-hoc PE prototypes can evolve into reproducible, practitioner-friendly workflows.