Watch: Grounding Classifier
Our model that predicts whether a query "deserves grounding" — whether an AI system will run a live web search to answer it.
Transcript
When a user types a prompt into an AI, the system has to make a split-second decision. Will it answer using its frozen memory, or will it run a live web search to find fresh information? This process is called grounding, and knowing when it happens is crucial. If an AI doesn't search the web for a specific query, your online content has zero chance of influencing its answer.
To solve this mystery, we built the Grounding Classifier. We started by sending ten thousand prompts to Google’s Gemini model with search grounding enabled. By recording exactly when Google chose to search the web and when it didn't, we were able to train a replica of Google's internal "query deserves grounding" classifier.
This model matches Google's default threshold for dynamic retrieval. It gives us a highly accurate tool to predict whether a prompt will trigger a live web search.
Understanding this distinction is vital. Grounded and ungrounded answers to the exact same question can look completely different. One is anchored to the live, shifting web, while the other is locked in the past. By predicting which path a query will take, you can target the search-enabled prompts where your content can actually make an impact.