Enhancing Software Requirements Engineering with Language Models and Prompting Techniques: Insights from the Current Research and Future Directions
Moemen Ebrahim, Shawkat Kamal Guirguis, Christine Basta · 2025
Large Language Models (LLMs) offer transformative potential for Software Requirements Engineering (SRE), yet critical challenges, including domain ignorance, hallucinations, and high computational costs, hinder their adoption.This paper proposes a conceptual framework that integrates Small Language Models (SLMs) and Knowledge-Augmented LMs (KALMs) with LangChain to address these limitations systematically.Our approach combines: (1) SLMs for efficient, locally deployable requirements processing, (2) KALMs enhanced with Retrieval-Augmented Generation (RAG) to mitigate domain-specific gaps, and (3) LangChain for structured, secure workflow orchestration.We identify and categorize six technical challenges and two research gaps through a systematic review of LLM applications in SRE.To guide practitioners, we distill evidence-based prompt engineering guidelines (Context, Language, Examples, Keywords) and propose prompting strategies (e.g., Chainof-Verification) to improve output reliability.The paper establishes a theoretical foundation for scalable, trustworthy AI-assisted SRE and outlines future directions, including domainspecific prompt templates and hybrid validation pipelines.