How do you keep an AI system working well after launch?
AI can quietly get worse over time. You can’t stop that from happening — you catch it early by testing it on a schedule, and fix it before anyone notices.
Read the full answer5 answers
Model drift.
Why systems that passed review degrade in production, and what catches it.
What causes model drift in LLM applications?
Three sources: the provider changing the model, your input distribution moving, and accumulated prompt edits. They look identical in the metrics and need different fixes.
Read the full answerHow often should you re-evaluate a model in production?
A fast subset on every deploy, the full suite nightly, and a human-reviewed sample weekly. Anything slower and you learn about regressions from users.
Read the full answerHow do you detect drift when you have no labelled data?
Monitor the inputs and the system's own behaviour: embedding distributions, refusal and escalation rates, tool-call mix, and answer length. All are label-free and move before quality does.
Read the full answerShould you pin model versions or take automatic upgrades?
Pin, then upgrade deliberately behind an eval run. Automatic upgrades trade a small maintenance saving for an unbounded, unobservable quality risk.
Read the full answerReady when you are.
Tell us one thing your business does by hand today. In two weeks, we’ll show you how superintelligence would do it.
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