Synthetic data is transforming the landscape of training foundational models such as GPTs and Stable Diffusion, by enabling the creation of diverse, privacy-conscious, and annotation-efficient datasets. In this illuminating session, we will trace the frontier of synthetic data generation. We'll discuss generative AI techniques that are reshaping industries, demonstrating how synthetic datasets created by LLMs, diffusion models, and hybrids can augment or even replace traditional human-curated data. We'll highlight the pitfalls of careless generation at scale, including the amplification of hallucinations and entrenched biases, and offer practical strategies for safeguarding data quality. You'll learn how to ground synthetic data in real-world contexts, leveraging distributional similarity metrics and LLM-as-a-Judge to reliably benchmark synthetic versus human data. Join us to discover how responsible synthetic data practices can drive a more robust, ethical, and innovative AI-powered future.