Large Language Models (LLMs) are increasingly integrated into software engineering (SE) workflows, supporting tasks such as code generation, test case derivation, and requirements-to-code traceability. These tasks heavily rely on natural language requirements, making the quality of those requirements a critical factor in LLM performance. Previous research suggests that quality defects, known as requirements smells, can negatively affect the accuracy and reproducibility of LLM-generated outputs. However, the extent and nature of this impact remain largely unexplored. The dissertation aims to investigate how requirements smells influence LLM effectiveness across various SE tasks and to explore automated techniques for detecting and mitigating such smells. Initial results indicate that increasing the number of smells in requirements significantly degrades LLM performance in traceability tasks. The dissertation aims to establish empirical foundations for improving prompt quality and to support more reliable use of LLMs in SE through automated quality assurance mechanisms.