In short. Grounding connects model responses to verifiable sources, with retrieval supplying context and citations linking answer passages to evidence.

Definition: Grounding connects an AI model's answer to verifiable sources of information. Those sources provide evidence for the response and can include web search results or an organization's documents, as described in Google Cloud's grounding overview.

Key facts

As of 17 October 2026, Google Cloud's grounding overview describes grounding as a way to reduce invented content and provide links for auditing responses.
The Gemini API documentation describes a search workflow in which the model decides whether a search can improve an answer, then generates queries if needed.
Google Cloud's RAG Engine overview describes adding retrieved information to the user's query as context for generating a response.
For a page to qualify as a supporting link in AI Overviews or AI Mode, Google's AI features documentation requires indexing and eligibility for a search snippet, the description or summary of a search result.

How does grounding work?

In Google's documented search workflow, an application enables the google_search tool when sending a prompt to the Gemini API. The model analyzes the prompt, runs searches when needed, and processes the results into an answer. A successfully grounded response includes citation annotations that link parts of the answer to sources.

The following configuration fragment matches the tool setting in Google's example:

{"tools": [{"type": "google_search"}]}

This fragment belongs in an API request. It shows how an application enables a source of information for the model. Google's documentation separately describes google_search_call steps containing the executed queries and google_search_result steps containing search suggestions for display.

Grounding also works with data outside public web search. Google's grounding overview lists connections to website data and stored documents through Agent Search, as well as external data sources through a search API.

How does grounding relate to RAG and citations?

Retrieval-augmented generation (RAG) supplies retrieved information as context when a model generates an answer. In Google's RAG Engine description, retrieval searches a knowledge base for information relevant to the query. Generation uses that material alongside the original query. Google's grounding overview lists RAG as a method for connecting a model to website data or documents.

Citations make the connection to sources visible. In the Gemini API response format, a url_citation annotation associates a source URL with a span of answer text identified by start_index and end_index. This provides a concrete basis for checking which source supports a particular passage. A practical review compares the annotated claim with the linked source.

What does grounding change for a website?

For website owners, Google's AI features documentation gives concrete access requirements for AI Overviews and AI Mode. A page must be indexed and eligible for a snippet. Google recommends allowing crawling through robots.txt and hosting infrastructure, and making important content available as text.

The same documentation says that these features require no special schema.org structured data. Existing structured data should match the visible text. Google also identifies the URL Inspection tool as a way to view the HTML Googlebot received while crawling a page.

FAQ

Does enabling search cause every prompt to trigger a search?

In the Gemini API workflow, the model first decides whether Google Search can improve the answer. It generates and executes queries when needed.

Can grounding use documents from my organization?

Yes. Google's grounding overview describes using RAG to connect a model to sets of documents stored in Agent Search.

How can I inspect the sources behind a grounded answer?

In a Gemini API response, you can inspect url_citation annotations for source URLs and the associated text spans. Open the linked source and compare it with the claim in the answer.

Sources

Check how a page reads to answer engines. Open the GEO score analyzer