Synthetic Data

Synthetic data is created by asking an AI model to generate examples — for instance, producing thousands of sample conversations, question-answer pairs, or edge-case scenarios that would be too time-consuming or costly to collect from humans. It’s increasingly common in both training and evaluation because it scales easily and can be targeted at specific gaps or scenarios. The risk is that synthetically generated data can inherit the biases or limitations of the model that produced it, creating a kind of feedback loop. For behavior architects, synthetic data is a useful tool for coverage — especially for rare or sensitive scenarios — but it should be treated as a complement to real data, not a replacement.