This description is based on a conversation with 'Sam', which took place over several days in December 2024
Despite the incredible size of the data used in model training, real-world datasets often contain gaps, biases, or privacy constraints that limit their effectiveness. In order to fill these gaps, AI model researchers turn to synthetic data—artificially generated information that statistically mirrors real-world data. Used primarily during the pre-processing and augmentation stages, synthetic data reduces reliance on sensitive or proprietary sources while enhancing dataset diversity.
Synthetic data is generated primarily by AI models, human experts, or simulation-based systems that recreate real-world conditions with controlled variables. It helps balance class distributions in models where real-world data is skewed, improves training on rare or extreme scenarios, and increases overall dataset robustness. By simulating diverse and uncommon occurrences, synthetic data ensures AI models can generalize more effectively across real-world applications. AI-generated text, images, and speech datasets also help fine-tune models for applications like chatbots, computer vision, and voice recognition. When designed correctly, synthetic data significantly enhances AI model performance while maintaining ethical considerations and privacy compliance.