The use of artificial intelligence (AI) in generating synthetic data has gained popularity due to its convenience, efficiency, and cost-effectiveness. However, the quality of synthetic data depends on the method used to generate it, with rudimentary methods resulting in unusable datasets that do not represent real-world data well. The article discusses the challenges faced by historical data generation methods, such as Generative Adversarial Networks (GANs), which struggled to produce realistic and complex synthetic data due to issues like mode collapse, difficulty in training, long-range dependencies, and the need for large amounts of data. In contrast, large language models (LLMs) like GPT-4 have democratized textual synthetic data by providing a simple yet powerful way of generating high-quality data through careful prompt designing, which can improve the authenticity of the generated data.