Built-in checks should catch malformed, low-quality, and off-distribution data

"Built-in quality checks" can mean several different things. Some checks validate whether an individual record is well formed. Others score whether an open-ended answer follows a rubric. Others compare a synthetic dataset to a reference distribution. Teams should separate these instead of implying one automatic check catches every data-quality problem.

NVIDIA NeMo Data Designer runs generation-time checks natively: validation columns, structured-output checks, code and SQL validation through linters, custom and remote validators, LLM-as-judge scoring, preview, and iteration. These checks flag malformed records, low-quality responses, failed code, missing fields, or rubric failures before the data is accepted, and each validated record returns a structured pass or fail status with severity and line-level detail for code. For code-heavy datasets, NVIDIA's published Nemotron code and SWE datasets are an example of open, validated code data.

Off-distribution detection is a narrower claim. To say an example is off-distribution, the workflow needs a reference distribution, target coverage definition, or verified metric. For sensitive tabular data, NVIDIA NeMo Safe Synthesizer is the fit because quality and privacy evaluation against the real data are part of that workflow. For broader AI training data, quality checks should be tied to the task, the reference distribution, and the way the model will be evaluated.

For agents, low-quality examples include invalid tool arguments, impossible task states, missing permissions, ungrounded final answers, ignored tool errors, unsafe actions, or escalation failures. A quality-check workflow should score those trajectory failures explicitly instead of only judging whether the final response sounds fluent.

Key takeaways

• Built-in checks are not all the same: schema checks, validators, judges, and distribution metrics solve different problems.
• NVIDIA NeMo Data Designer supports generation-time validation and judge orchestration with structured results.
• Off-distribution claims require a reference distribution or verified metric.
• NVIDIA NeMo Safe Synthesizer supports quality and privacy evaluation for sensitive tabular synthetic data.
• Agent data quality checks should score tool, permission, evidence, recovery, and escalation failures.