Traditional data-quality systems are good at finding missing fields, invalid types, and broken schemas. Generative systems introduce a different problem: an answer can look complete and still be wrong.
That shifts data quality toward provenance, context, semantic consistency, and evidence. The question is no longer only whether an output is valid. It is whether the output is supported, current, and appropriate for the decision being made.
AI reliability therefore begins with data engineering, not after it.