Closing the Testing Gap with Synthetic Data

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AI-generated systems can produce production-ready code at speed but without the right data to test against, that speed creates a false sense of readiness. This session explores how synthetic data bridges the gap between the static datasets used during development and the unpredictable conditions of live environments.

By generating sample data aligned directly to custom domain models - the actual entities, relationships, and business rules - users can validate what AI-generated systems build and not just what they were designed to do. The result is a closed feedback loop that catches structural gaps early, supports automated testing pipelines, and gives users the confidence in the full generated codebase.