methodology-review

To test if AI coding quality gates are load-bearing, run ablation testing via controlled A/B comparisons to measure whether removing a specific gate degrades correctness, completeness, or spec conformance. This yields a structured verdict to keep, simplify, or remove the component.

Review your AI coding workflow methodology when upgrading model generations, after repeated zero-findings outcomes, or on a regular quarterly cadence. This prevents your methodology from silently degrading as model capabilities change over time.

Ablation testing for AI-generated code quality is a periodic validation process that measures whether removing or simplifying a workflow component degrades correctness, completeness, or spec conformance, ensuring you only cut rules that are proven redundant.

Update methodology documentation by logging ablation testing findings to your repository learnings artifact without tag-filtering, outputting a structured LOAD-BEARING or NOT NEEDED verdict, and optionally submitting universal lessons to a Learnings Hub for reuse.

Decide where new AI coding checks should live by evaluating routing guidance based on how they are invoked and enforced. Scope-appropriate routing determines whether new checks should live in rules, workflows, skills, or scripts.

Yes, you can run methodology reviews without external dependencies. The review process operates independently to inventory learnings, run controlled A/B comparisons for detection and false positives, and output structured verdicts without requiring external modules.