AI & Code Quality

Code review designed for human-written code catches only 47% of AI code issues. Here's the 3-layer review stack built for an AI-first world with measurable results.

A blanket ban on AI coding tools is as reckless as uncontrolled adoption. The RAPID decision framework helps engineering leaders choose the right control tier.

Most AI code quality advice fails in regulated industries. After consulting for fintech, healthtech, and automotive, here's the framework that passed 11 regulatory audits.

AI-heavy codebases have 3x the code duplication of non-AI codebases. After auditing 9 codebases, here's a 3-layer framework to prevent AI-introduced duplication.

After reviewing 500+ AI-generated snippets, I built the CRAFT prompt framework. Prompts with constraints produce acceptable code 82% of the time vs 45% without.

After 847 tracked AI pair programming sessions across 6 tools, here's what actually works and what wastes time. Data-driven results with a 3-question filter framework.

AI coding assistants are systematically biased toward insecure code. After auditing 14 codebases, here are the 6 vulnerability patterns AI generates most and how to stop them.

Not all AI code needs the same review depth. Here's a data-backed decision matrix for when to trust AI output, when to modify it, and when to rewrite from scratch.

After working with 22 engineering teams, I built a 5-level maturity model for AI code quality. Most teams overestimate their level. Here's how to assess and advance.

Standard CI/CD checks miss AI-specific code issues. Here's how to build a 4-stage quality gate that caught 73% more issues before production for 8 engineering teams.

Our PR approval rate dropped from 78% to 52% after adopting Copilot. After redesigning our review process with 5 key changes, we recovered to 74%.

AI coding assistants are making individual engineers worse while making teams more productive. Here's the data from 83 engineers and a framework to prevent deskilling.

A battle-tested checklist for reviewing AI-generated PRs, organized by priority. Every item caught a real bug in a real codebase. Print it, pin it, use it.

AI tests hit 91% coverage but only 34% mutation score. Here's proof that AI-generated tests create dangerous false confidence, and the workflow to fix it.

Most teams track the wrong AI code metrics. Here are the 8 metrics that actually predict whether AI code generation helps or hurts your codebase long-term.

Vibe coding works 80% of the time. That other 20% is where your codebase dies. Here's how to detect it, assess the damage, and break the habit.

Everyone tracks AI productivity metrics. Nobody tracks what happens when that code needs changes. Here's data from tagging 1,247 PRs over 8 months.

AI code looks clean but hides real issues. Here's the REVIEW method: a 6-step process specifically designed for reviewing AI-generated PRs efficiently.

AI coding tools are creating invisible technical debt. After auditing 6 codebases, here are the patterns I found and the framework to assess your own AI debt.