AI-generated systems can produce production-ready code at speed, but without the right data to test against, that speed can create a false sense of readiness.
This session explores how synthetic data bridges the gap between static development datasets and the unpredictable conditions of live environments. By generating sample data aligned directly to custom domain models, teams can validate what AI-generated systems build, not just what they were designed to do.
What You'll Learn
- How synthetic data closes structural testing gaps in AI-generated systems
- How to align generated datasets to domain entities, relationships, and rules
- How to build closed feedback loops for confidence before deployment