Quality Monitoring

Course overview

Quality monitoring: online and offline evaluation strategies, prompt and version tracking, feedback collection and annotation workflows, and regression gates before deployment to prevent performance degradation.

Compares online versus offline evaluation approaches to measure AI output quality in context.

Covers prompt and component version tracking to correlate changes with behavior shifts.

Outlines feedback collection and annotation pipelines to generate labeled signals for monitoring.

Explains regression gates used to ensure no quality regressions are introduced prior to deployment.