The rapid evolution of software systems, particularly multi-component architectures, has intensified the need for efficient and context-aware testing solutions. Traditional methods of test case generation often struggle to account for the dynamic interdependencies between system components, which can lead to incomplete or redundant test coverage. Leveraging Large Language Models (LLMs), such as GPT-based architectures, presents a novel approach to generating context-aware test cases that better reflect real-world usage scenarios and system interactions. By fine-tuning LLMs on domain-specific knowledge and historical test data, we can enhance their ability to generate meaningful, diverse, and context-sensitive test cases. This abstract discusses a methodology for utilizing LLMs to automatically produce test cases tailored to multi-component systems, accounting for both individual component behaviors and their interactions. We highlight the benefits of this approach, such as improved test coverage, reduced manual effort, and faster development cycles, while also addressing challenges like ensuring test case quality and managing model biases. Ultimately, this research aims to establish a robust framework for integrating LLMs into the software testing lifecycle, with the goal of enhancing software reliability and reducing time-to-market for complex systems.