Grounding an AI answer in the content of a specific web page you supply, rather than in results from an open web search.
Document grounding is when an AI model grounds its answer in the content of specific pages you hand it, rather than in results from an open web search. You supply one or more URLs, the model fetches and reads each page's main text and title, and it answers from that.
Google delivers this through its URL Context tool, an internal browse capability that prints a page's content and title for Gemini to read. OpenAI's models had this ability earlier; for Google it was a notable step beyond search-only grounding.
For content owners this raises the bar on machine readability: if a model is going to read your page directly, the main content needs to be cleanly extractable. It sits alongside broader grounding and the newer content fetcher path for pulling multiple structured sources at once.
