Glossary
Model Drift
Model drift is a change in an AI model’s behavior or outputs over time, caused by model updates, retrieval changes, safety tuning or other system changes. In visibility measurement, drift can alter answers even when a brand and its content have not changed.
In plain terms
The engine changes, so the same question may start getting a different kind of answer.
Why it matters
Teams must distinguish improvements caused by their work from broad changes affecting every brand in an engine.
How to apply it
- Record the model and collection date.
- Maintain control prompts outside the target category.
- Compare movement across competitors and engines.
Example
After an engine update, citation counts fall across every tracked vendor, indicating platform drift rather than one brand’s loss.
Sources
Related reading
Related terms
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Ranking is no longer enough
You need to be cited, mentioned, and recommended.
Being cited, mentioned, and recommended are three different outcomes, and most brands only ever achieve the first one. Ranking is no longer enough because AI engines answer buyers directly and name only a short list of vendors as the recommendation — everyone else is cited in passing, if at all. Get a free AI Visibility Report to see exactly where your brand appears today across ChatGPT, Google AI Overviews, Gemini, Perplexity, and Copilot, where competitors are winning the recommendation instead, and what's keeping you from moving up the shortlist.