A process built around a particular model will eventually meet a change nobody asked for: an update that alters behaviour, or a retirement notice. Building so that the change is absorbed rather than fatal is a matter of separation.
Keep the instructions out of the workflow
Prompts embedded across scripts and documents must all be found and edited when behaviour changes.
Holding them in one place, versioned, means an adjustment is made once and its history is visible.
This also makes it possible to say which version produced a given output, which becomes necessary the first time a result is questioned.
Build the evaluation set before you need it
Keep twenty to fifty real inputs with their correct outputs, and run them whenever anything changes.
Without this, a provider's update is detected by a user noticing that results have got worse, which is late and unconvincing to anyone you report it to.
The set does not need to be large. It needs to include the cases that actually matter, particularly the awkward ones.
Pin the version and read the deprecation notices
Where a provider offers dated model identifiers, use them rather than an alias that silently moves to the newest release.
Pinned versions are retired on a published schedule, which converts a surprise into a deadline you can plan around.
Aliases are convenient and hand the timing of every change to someone else.
Separate what is model-specific from what is not
Most of a working process is document handling, routing, storage and review, none of which depends on which model is called.
Keeping the model call behind a single boundary means switching providers is one change rather than a rebuild.
It also makes running two models in parallel practical, which is how a replacement is evaluated honestly before it is adopted.
Assume the output format will drift
Processes that parse generated text break when phrasing shifts, and phrasing shifts with every model update.
Requesting structured output and validating it against a schema turns a silent corruption into a visible failure that can be retried.
The general principle is worth stating plainly: anything downstream should fail loudly on unexpected input rather than proceeding with something it half understood, because a workflow that quietly produces wrong results is far more expensive than one that stops.