Switching models without breaking everything

This isn't a possibility, it's a timeline: versions get deprecated, new ones come out, and a migration will be imposed on you. The only question is whether you'll have prepared for it.

Why this isn't a simple update

A new model is better on average and different everywhere. Your prompts have been tuned to the old one's habits: its verbosity, its formatting style, its refusals. A more capable model can very well degrade your specific product, because it no longer reacts the same way to your instructions.

Overall performance says nothing about performance on your use case.

The evaluation set, which you need to have built beforehand

One hundred to three hundred cases drawn from real traffic, with the expected output or a validation criterion. Deliberately include the awkward cases: empty inputs, very long ones, in another language, hostile ones. These are what reveal regressions.

Without this set, a migration is a gamble. With it, it's a decision that takes an hour.

Migrating in parallel

Run the new model alongside the old one on real traffic, without serving its responses. You compare under real conditions, at controlled cost, before exposing anyone to it. Then switch over gradually, while monitoring your signals.

The tuning trap

Don't rewrite your prompts at the same time as you change models. Two variables moving together make any diagnosis impossible. Migrate first with prompts held constant, measure, then optimise.

What to keep on hand

The ability to revert to the old model via one configuration variable, as long as it's available. And the announced deprecation date, somewhere someone actually looks.