AI systems learn from examples, but for some tasks real examples are rare, sensitive or expensive to collect. Synthetic data, information generated on purpose to resemble the real thing, has become a common way to fill those gaps, for instance creating varied practice cases for a system that must recognise rare situations.
Used carefully, synthetic data can protect privacy and cover situations that seldom appear in real records. The risk is that poorly made synthetic data teaches a model patterns that do not hold in the real world. Teams therefore mix it with genuine data and test the result against reality, treating it as a supplement rather than a full replacement.