Grounding is a provider feature — for example Grounding with Google Search — that connects the model to real-time web content and returns grounding metadata (the search queries it ran and the sources it used)1. Its stated purpose is to reduce hallucination and let the model cite verifiable sources beyond its training cutoff.
Rhinegold's caution: grounding is a mechanism, not a visible outcome. A grounded answer does not guarantee a visible citation — grounding metadata is frequently not surfaced to the end user, and providers expose it inconsistently. Do not infer a citation from the fact that grounding happened, and do not infer that grounding did not happen from the absence of a visible citation.
Grounding is the right lens for understanding why a brand does or does not get cited: it determines the candidate pool of sources an answer can draw on in the first place.
Grounding internals are largely opaque — providers do not publish grounding logs, so you observe its effect (citations) rather than the mechanism, and behaviour is provider-specific. You are always inferring grounding from its downstream traces.
The downstream Citation Rate is observable, and where a provider does expose grounding metadata, the set of domains it pulled is too — enough to reason about the source pool without seeing the mechanism directly.
Against Citation Rate, which is the visible attribution — grounding is the retrieval step beneath it. Against Mention Rate, which is mere presence. And against GEO, where improving retrievability is how you try to enter the grounding pool.
Providers define and expose grounding differently, and there is no cross-provider standard for grounding transparency — so grounding behaviour cannot be compared like-for-like across engines.