Fast AI-assisted shipping hides a cost that only shows up months later: systems wired so tightly to one model's quirks that any upgrade forces a rewrite instead of a swap.
The Details:
Treat the model as a replaceable part, not the structure holding everything up. Teams that last build clean separation between AI calls and application logic, so a new model version slots in without touching core code. Teams that struggle bury model-specific behavior deep inside functions until nothing can be changed without unraveling the whole thing.
Prompts need to live as real files, not strings spliced together at runtime. Give them names, put them under version control, and write a short note on what each one does and who owns it. A prompt change deserves the same caution as a database schema migration, because functionally it is one.
Build a small evaluation set before pressure forces you to. Ten sample cases in a plain file beat nothing, because they give you a fixed reference for what correct output looks like. Without that reference, every model update turns into guesswork instead of a checkable test, and teams end up debugging a vague feeling instead of a specific failure.
Keep tool schemas plain and predictable rather than clever. A stable, boring interface survives a model swap; a clever one usually becomes the thing you regret in six months. The same logic applies to human oversight: someone needs to actually read the diffs and check whether new AI-generated code matches how the rest of the system already solves that problem, because unreviewed drift compounds silently across a codebase.
Bottom Line: Speed without seams just moves the cost forward; the systems that survive are the ones built so the model can change without anything else having to.
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