Ground truth sources, regression suites, and synthetic data

This is Lesson 3.2: Ground truth and regression. The AI system is fully instrumented. Every failure mode is visible in the traces. There's observability. There's a failure taxonomy. Now something else is needed: verified correct answers to measure against. That's what ground truth is, the foundation of every metric built in this course. The reasoning is straightforward. Accuracy can't be measured without knowing what "correct" looks like. Sounds obvious. But teams routinely skip this step and build entire eval pipelines on unverified expected answers. The pipeline runs. The numbers look good. And then production users start complaining. That's what happens when ground truth is an afterthought. This lesson covers how to source ground truth strategically, construct a regression suite that gates every release, and generate synthetic data to fill coverage gaps. Three things: sourcing, constructing, filling gaps. By the end, the path to building a regression suite that gives confidence before every release will be clear.