How do your teams manage AI-generated code without losing control?

I’m curious how teams are adapting AI coding tools like GitHub Copilot or Cody in daily workflows.
We found them super fast individually — but at the team level, issues popped up:

Code quality and architecture drifting
Security risks from AI-added de…


This content originally appeared on DEV Community and was authored by jarzebowsky_dev

I’m curious how teams are adapting AI coding tools like GitHub Copilot or Cody in daily workflows.
We found them super fast individually — but at the team level, issues popped up:

  • Code quality and architecture drifting
  • Security risks from AI-added dependencies
  • Misaligned features vs. Jira tickets
  • Difficult code reviews from AI output

How are you managing these as a team? Do you review AI code differently? Do you have process checks in place?

Would love to hear your experience.


This content originally appeared on DEV Community and was authored by jarzebowsky_dev


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