PM AI Versioning
(2026 Edition)
Managing AI versioning starts with pinning production model versions instead of auto-upgrading, running any new model in shadow before promoting it, and gating every version change behind the eval suite, since vendors like OpenAI, Anthropic, and Google update underlying models periodically and products that skip pinning or shadow tests see silent regressions on a monthly basis.
By Naman Goyal · Product manager · Builder of PM Streak · Updated July 3, 2026
5 practices and 4 traps for AI versioning.
Build AI Versioning PM Skills — Free →5 Practices
Pin model versions in production — automatic upgrades break things
Run new models in shadow before promoting
Eval suite is the gate for any version change
Communicate model upgrades to enterprise customers
Plan for model deprecation by vendors
4 Traps
Auto-upgrading to latest model — breaks behavior
No prompt version control
Skipping shadow tests because 'evals look fine'
No rollback path when a model regresses
FAQ
How often do silent model changes break products?
More often than vendors admit. OpenAI, Anthropic, Google all update underlying models periodically. Production AI products that don't pin or shadow-test see silent regressions monthly. PMs who treat models as code dependencies — versioned, tested, deprecated deliberately — ship more reliably.
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