Enhancing Software Requirements Engineering with Language Models and Prompting Techniques: Insights from the Current Research and Future Directions

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.