Synthetic Data

Synthetic data solves a critical bottleneck in AI development: getting enough high-quality training data. Real data is often limited (rare diseases), expensive to label (expert annotation), private (medical records, financial data), or biased (underrepresenting certain groups). Synthetic data fills these gaps. Generation approaches: (1) LLM-generated — use GPT-5.2 or Claude to generate training examples. Ask the model to create diverse examples of customer support tickets, product reviews, or code samples. (2) Rule-based — programmatically generate data following known patterns (synthetic financial transactions, test user profiles). (3) GANs/Diffusion models — generate synthetic images, audio, or video. (4) Simulation — physics engines generate synthetic sensor data for robotics and autonomous driving. Quality is the key challenge. Synthetic data must be diverse enough to represent the real distribution, accurate enough to teach correct patterns, and balanced to avoid introducing new biases. Best practice: generate synthetic data to augment (not replace) real data, validate synthetic data quality against real data benchmarks, and watch for model collapse when training exclusively on synthetic outputs.