Practical thinking on validation strategy, engineering maturity, and what it actually takes to ship AI-generated code with confidence.
A practical four-phase framework engineering leaders can implement this week — define validity, build your gates, assign ownership, and measure what matters.
Read article →AI-generated code ships fast — but without a systematic review process, it ships with gaps. A practical six-point checklist for engineering leaders.
Read article →AI-generated code that ships without proper test coverage looks deceptively clean. Here are five signals your test suite isn't actually protecting you.
Read article →A framework for measuring AI code quality across five dimensions — coverage, validation depth, review rigor, production monitoring, and team capacity.
Read article →AI coding tools accelerate output. Most engineering teams haven't updated their review process to match. Here's a practical 5-step self-audit — specific enough to surface real gaps, fast enough to run in a single afternoon.
Read article →Your team is running tests. The CI pipeline is green. And yet AI-generated code is still causing regressions, compliance gaps, and architecture drift. Here's why testing and validation are not the same thing — and what that distinction means for engineering leadership.
Read article →Take our free 10-question assessment to benchmark your AI validation maturity across three dimensions.
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