The failure mode is predictable. A team ships an impressive assistant, usage grows, the inference bill arrives, and the feature gets quietly disabled. Avoiding that is mostly a scoping exercise, not an engineering one.
Pick a narrow problem with a checkable answer
'Summarise this support thread into three bullet points' is a good scope: narrow, frequent, and you can tell whether the output is right. 'An AI assistant for our app' is not a scope, it is a mood.
Build the evaluation set before the feature
- Collect 50–100 real inputs from your actual data
- Write the expected output, or the properties a good output must have
- Score every prompt change against the set, not against vibes
- Keep a held-out slice you never tune against
Budget per user, not per month
Work out the token cost of one typical interaction, multiply by the interactions a heavy user will make, and check that number against what that user pays you. If it does not clear comfortably, cache aggressively, route routine calls to a smaller model, or cap the feature.
Hard per-user rate limits are not a degradation. They are the thing that keeps the feature shipped.
Plan the failure path
Models time out, refuse and occasionally return nonsense. Every AI surface we ship has a defined fallback: a cached answer, a deterministic path, or an honest message. Silence is the one option we never take.
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