Data is the fuel that drives the testing and development of AI/ML applications. Whether for machine learning models, generative AI systems, or multimodal and large language models (LLMs), synthetic data enables rapid iteration, secure testing, and reliable performance assessments. Poorly designed test data can limit coverage of real-world scenarios, leading to unreliable outcomes. By leveraging synthetic data, teams can overcome challenges associated with real-world data acquisition while maintaining the high-quality standards required for machine learning, deep learning, and reinforcement learning systems