Grounding

How does grounding work in AI search?#

Grounding works by retrieving sources at the moment of the question, placing them in the model’s context, and attaching each claim in the answer to the source that supports it — so every citation marks where a fact came from.

Google defines grounding as “providing content from the Google Search index to the model at prompt time to improve factuality and relevancy” (Google’s crawler documentation). The Gemini API documentation lays out the loop: the model decides whether a Google Search would improve the answer, writes one or more queries, and returns an answer whose citations each link a span of text to a source URL. Google’s stated aim is to “reduce model hallucinations by basing responses on real-world information.”

• Claude always cites when it searches, and each citation carries up to 150 characters of the text it relied on, per Anthropic’s documentation.
• Gemini grounds in the Google Search index, and Google says that when it quotes a large amount of text from a page, it always links to that page. Disallowing Google-Extended opts you out of Gemini-app grounding.
• Google AI Overviews use core ranking to retrieve pages, and a Gemini model writes an answer that links to them — retrieval-augmented generation under Search’s quality systems.
• Perplexity shows numbered source cards above every answer, built from the passages its rerankers selected.

Grounding vs RAG: what's the difference?#

RAG is the mechanism — retrieve documents, then generate — while grounding is the result: an answer whose claims are tied to specific sources a reader can check. Most grounding in AI search is done with RAG, but retrieving a page doesn’t guarantee citing it.

The distinction matters when you measure. A page can be retrieved and read without being cited, because the model attaches citations only to the passages it actually relied on — usually the ones that stated the fact most directly. Retrieval puts you in the room; grounding is the moment the answer points at you and becomes an AI citation.

Grounding also has a scope beyond web search. A user can ground an answer in a document they upload, and enterprise tools ground in internal files. In AI search, though, grounding sources come from the web, which is why the engine’s index and crawler rules decide who can be cited at all.

How do you make content easy for AI to ground on?#

Make content easy to ground on by stating each important fact in one self-contained sentence that names the subject, gives the specific figure or detail, and can be checked against a source — the shape a model can attach a citation to.

1. One fact, one sentence. Claude’s citations quote at most 150 characters. “Plannora’s Team plan costs $8 per user per month” can be cited; a paragraph that builds up to the price can’t be cited cleanly.
2. Name the subject every time. A grounded sentence travels without its neighbors, so “It costs $8” grounds nothing.
3. Show where your facts come from. Link primary sources and date your data. The original GEO study found that adding citations, quotations and statistics raised a source’s visibility in generated answers by up to about 40% (Aggarwal et al.).
4. Be the primary source for your own facts. Pricing, specs, policies and integrations belong on clearly titled pages on your domain. Anthropic tunes Claude’s Research mode to prefer primary sources over content farms.
5. Keep facts in the visible text. Facts that exist only in JSON-LD may never reach a model that reads page text, so state them in the copy as well as in schema markup.

How do you check whether an AI answer is grounded in your page?#

Check grounding by running the buyer prompt, opening each citation, and matching every claim about your brand to the passage it points at — then noting which claims rest on your pages, which on third parties, and which have no source at all.

1. Run the prompt in each engine you care about, with search on, and save the answer with its citations.
2. Split the answer into claims about your brand: price, features, who it’s for, how it compares.
3. Match each claim to its citation. Claude’s citations quote the text they rely on; in Gemini and ChatGPT, open the link and find the passage.
4. Tag each claim as grounded on your page, grounded on a third party, or ungrounded. Ungrounded claims about you are where AI hallucinations live.
5. Repeat on a schedule. Citations shift between runs — in Attrifast’s 2026 study, only about half of Claude’s cited domains repeated across three runs of the same prompt — so judge trends, not one screenshot. That’s prompt tracking.

Related terms#

• RAGHow LLMs answerRetrieval-augmented generationA technique in which an AI system first retrieves relevant documents from an index and then gives them to a large language model to write its answer, so the response can cite current sources instead of relying only on what the model memorized in training.Read the entry
• AI search & GEOAI citationA link or source reference that an AI answer engine attaches to its response to show where a claim came from, and it differs from a brand mention in that it points to a specific page that can be clicked and can send traffic.Read the entry
• How LLMs answerAI hallucinationA confident but false statement produced by a large language model — an invented statistic, feature, price or citation — which happens because the model generates plausible text rather than looking facts up.Read the entry
• Content & relevanceInformation gainThe new information a page adds beyond what other pages on the same topic already say — original data, first-hand experience, a new angle — and it is the leading explanation for why content that rewrites the top results struggles to rank or be cited.Read the entry
• Content & relevanceAnswer-first contentA writing structure that puts the direct answer to a section’s question in its opening sentence and adds context and evidence after it, so readers and AI retrieval systems can take the answer without reading further.Read the entry
• AI crawlersGoogle-ExtendedA robots.txt product token, not a separate crawler, that lets site owners stop Google from using their content to train Gemini models and to ground answers in the Gemini app, without affecting Google Search — including AI Overviews and AI Mode.Read the entry