eval

Validating project outcomes against OKRs requires deriving eval sets from PKRs and executing a scoring harness to produce actionable pass/fail signals. This standardized mechanism verifies whether your projects meet their defined objectives.

Regression detection for project quality drift compares current scoring results against a frozen baseline to surface regressions across quarters. This automated process identifies performance gaps and triggers governance workflows to address degraded quality.

Running a close-eval involves triggering eval-set derivation from PKRs and invoking a scoring harness against the project artifact. This produces per-project results and quarterly baselines to verify performance against OKRs.

Executing a benchmark-sweep requires derived eval items from PKRs, a scoring harness, and regression-detection tools. These components work together to produce per-project results and quarterly baselines for automated quality verification.

Integrating project evaluation results with governance workflows drives action on red findings via QA gates, ADRs, and quarterly reviews. This auditable mechanism ensures quality drift and performance gaps trigger immediate governance responses.

Proving whether a project approach works requires deriving eval sets from PKRs and running automated scoring to produce pass/fail signals. This standardized verification quantifies project outcomes and identifies performance gaps against OKRs.