What it means
An AI quality check is a review activity that evaluates whether AI-generated outputs are accurate, relevant, complete, explainable enough for the workflow, and safe to use. In HR tools, quality checks may apply to candidate summaries, interview notes, job descriptions, employee communications, workforce insights, matching results, classifications, or workflow recommendations.
Why buyers should care
AI output can sound confident even when it is incomplete or wrong. Quality checks help teams catch errors before outputs influence hiring, employee communication, analytics, or operational decisions. They also create feedback that can improve configuration, prompts, data inputs, training, and workflow design. Buyers should expect quality checks to be part of normal operations, not only an implementation task.
Evaluation checks
Review whether the product supports sampling, reviewer comments, output comparison, confidence signals, correction workflows, and reporting on recurring issues. Ask vendors how low-quality outputs are flagged, whether corrections are saved, and how administrators learn from patterns. Quality checks should be tied to workflow risk and should preserve evidence of who reviewed or changed an AI output.
This glossary entry is buyer-oriented guidance, not legal, compliance, or financial advice.