Section 043 · Chapter 6, Building Evals That Matter
Building a Quality Metric
Every AI team needs at least one quality metric that turns messy behavior into release evidence.
quality metricbuilding quality metric
What to do
- Define sub-scores, weights, hard blockers, slice reporting, confidence intervals, and minimum practical improvement before the comparison.
- Define runnable checks that exercise quality metric and building quality metric.
- Set acceptable outcomes and blocker failures for quality metric and building quality metric before running the evaluation.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for quality metric, building quality metric needed to reproduce work on Building a Quality Metric.
- Report results for quality metric, building quality metric by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In production work, separate metric design from release thresholds. Define sub-scores, weights, hard blockers, slice reporting, confidence intervals, and minimum practical improvement before the comparison. A good metric is not truth. It is an explicit decision instrument that can be challenged, audited, and improved.
Continue the conversation
Apply this to your context.
Save your product context once, then open a focused conversation that combines it with this concept.
Cite this page
Jason Arbon. "Building a Quality Metric." Testing AI Knowledge Edition, section 43.
https://jarbon.ai/testing-ai/knowledge/ch043-building-quality-metric.html