Synthetic Data

Synthetic data solves the chicken-and-egg problem of AI development: you need data to train models, but collecting and labeling real data is expensive and slow. By using AI to generate realistic training examples, you can bootstrap model development, augment sparse datasets, and create data for scenarios that are rare in production but critical to handle correctly.

Common approaches include using LLMs to generate text examples (prompting GPT-4 to create customer support conversations covering edge cases), using diffusion models to generate training images, and using statistical methods to create tabular data that preserves the distributions and correlations of real datasets while protecting individual privacy. The quality of synthetic data depends heavily on how well it captures the complexity and edge cases of real-world data.

For product teams, synthetic data is increasingly a practical tool rather than a research curiosity. Use it to expand small labeled datasets before fine-tuning, generate diverse test cases for evaluation pipelines, create training examples for rare but important scenarios (fraud, safety violations), and build development datasets when real user data has privacy restrictions. The critical step is validating that models trained on synthetic data perform well on real data, since synthetic data can introduce subtle distributional biases.