Prompting Strategies for Large Language Models in Requirements Elicitation : A Systematic Literature Review

Abstract

Recent advancements in Large Language Models (LLMs) such as GPT-4, DeepSeek, Gemini, and LLaMA have introduced new opportunities for supporting requirements elicitation, one of the most knowledge-intensive and error-prone activities in Requirements Engineering (RE). The effectiveness of LLM-assisted elicitation depends largely on how prompts are designed, structured, and contextualized. As a wide range of prompting strategies has emerged, including zero-shot, few-shot, chain-of-thought, role-based, and prompt-chaining techniques, there is a growing need to consolidate current evidence and evaluate their strengths, limitations, and practical relevance. This thesis presents a systematic literature review of 58 peer-reviewed studies investigating prompting strategies used with LLMs for requirements elicitation. The review follows established SLR guidelines and PRISMA 2020 procedures. Studies were retrieved from Scopus, IEEE Xplore, and the ACM Digital Library using a unified search strategy and underwent a structured screening, quality assessment, and data extraction process. The findings categorize prompting strategies across multiple dimensions, including their structural design, reasoning mechanisms, and integration with supporting tools such as retrieval-augmented generation pipelines, conversational agents, and domain-specific knowledge models. The review also synthesizes how prompting is applied across key elicitation contexts. Furthermore, it examines evaluation approaches, highlighting trends in quantitative metrics, expert judgment, and mixed-method assessment. The study contributes a consolidated view of prompting practices in LLM-based elicitation, identifies recurring benefits and challenges, and outlines emerging best practices. By synthesizing methodological patterns and gaps, this review offers guidance for researchers and practitioners seeking to design effective prompting strategies and develop reliable, human-centered elicitation workflows supported by generative AI.