How Do You Define Code Quality? A Practical Framework for Scalable AI Code Quality
Most engineering teams don’t have a quality problem; they have a definition problem. When “good enough to ship” means something different to every developer, every team, and every AI tool in the stack, standards decay faster than code does.
This guide gives engineering leaders a practical framework for defining, enforcing, and measuring code quality at scale. It covers how to classify repositories by business risk, apply the right checks at the right checkpoints across the SDLC, and move from static rules to adaptive standards that evolve with your codebase. You’ll walk away with:
- A concrete model for the three dimensions of code quality
- A risk-tiering approach for prioritizing enforcement
- A set of outcome metrics that show whether your quality system is actually working, not just running.
