Originally published on AI Tech Connect.
What you need to know Swapping the model behind a live LLM product is one of the most deceptively dangerous changes a team can ship. The provider announces a stronger, cheaper successor, someone changes one line of configuration, and a fortnight later support tickets climb, a downstream JSON parser starts failing intermittently, and nobody can point to the commit that caused it. The uncomfortable truth is that a model which wins on every public benchmark can still be a regression for your application, because your prompt, your few-shot examples and your output contracts were all quietly tuned to the model you already had. The good news is that the deployment discipline the platform-engineering world spent a decade building for services — shadow traffic, canary ramps, automated rollback —…
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