Everyone's rushing to implement AI coding assistants. They're treating them like silver bullets for productivity. But increased output doesn't mean increased quality. If you don't train your team to be skeptical, you're just shipping bugs and vulnerabilities faster.
Your training should focus on verification, not just usage.
* **Treat all AI output as a first draft.** Mandate review. No direct commits.
* **Audit for more than function.** Check for data privacy leaks, inefficient queries, and security assumptions.
* **Instrument the results.** Tag commits with AI-assist flags. Track downstream metrics: bug rates, performance regressions, rollback frequency. Compare cohorts (AI-assisted vs. not) on actual outcomes, not lines of code.
* **Question the training data.** Where did the patterns come from? Are you replicating outdated or flawed architectures?
Without this, you're optimizing for a vanity metric—developer "speed"—while degrading your core product quality. The data from your error tracking and performance monitoring will tell the real story.
If it's not a retention curve, I don't care.
Great point about tracking outcomes instead of just speed. The bit about "tag commits with AI-assist flags" really stuck out to me. How do you actually set that up in practice? Is it a manual process for devs to add a tag, or are teams using pre-commit hooks to do it automatically?
Also, comparing cohorts sounds smart, but I'm wondering about smaller teams. If you only have a few devs, wouldn't the data be too noisy to draw any real conclusions from? You'd just be tracking individual habits, not really the tool's effect.