Our model that predicts whether a query "deserves grounding" — whether an AI system will run a live web search to answer it.
Grounding Classifier is our model that predicts whether a prompt will trigger grounding — that is, whether an AI system will run a live web search to answer it or reply from its trained-in memory alone. Knowing this in advance tells you which queries your content can actually influence.
We built it using the same technology behind AI Rank: we prompted Google's Gemini 2.5 Pro with search grounding enabled across 10,000 prompts, recorded whether Google grounded each one, and trained a replica of Google's internal "query deserves grounding" classifier. This matches Google's documented default dynamic retrieval threshold of 0.3.
The result is a fine-tuned binary classifier we use across our machine-learning pipelines, demonstrated publicly in our QDG tool. It matters because grounded and ungrounded answers to the same question can differ drastically — one is anchored to fresh web sources, the other to the model's frozen parameters.
