# Grounding in AI: what it is and how it enables citations

> What grounding is in AI, how it connects a model to a search engine's index, what Google-Extended controls and how to see if your store is used as a source.

URL: https://clione.ai/glossary/grounding/
Lang: en

Search for "grounding" and you will get three unrelated things: the earth connection of an electrical installation, a psychology technique for calming anxiety and the one that matters here, the one from artificial intelligence. In AI, grounding is **the moment an assistant stops answering from memory and goes to look something up**. For a store it is the moment that counts, because it is when your [product page](/glossary/product-page/) can make it into an answer and get cited. Without grounding, the model answers with what it learned in training and has no specific source to link.

## How it works

A [language model](/glossary/llm/) keeps what it learned in its parameters. The paper that named [retrieval-augmented generation](/glossary/rag/) ([RAG](/glossary/rag/)), by Patrick Lewis and eleven other authors and presented at NeurIPS 2020, called that parametric memory, and added a non-parametric memory to it: a Wikipedia index queried with a trained retriever. The result was that **models with retrieval generated language that was more specific, more varied and more factual** than the same kind of model with parametric memory alone.[^rag-paper]

Google defines grounding in its [crawler](/glossary/web-crawler/) documentation: giving the model content from Google Search's index at the moment of the question, to improve the accuracy and relevance of the answer.[^google-crawlers] In its guide to Google's [AI search](/glossary/ai-search/) features it uses it as a synonym for RAG: a technique that improves the quality, accuracy and freshness of answers by relying on its usual ranking systems to **retrieve relevant, current pages from its index**. Then its systems review the specific information on those pages to generate a more reliable answer.[^google-ai-guide]

The [Gemini](/glossary/gemini/) API shows the process step by step when Google Search is turned on as a tool:[^gemini-grounding]

1. The app sends the user's question to the model with the search tool turned on.
2. The model analyzes the question and decides whether a Google search can improve the answer.
3. If so, it generates the queries and runs them itself.
4. It processes the results, synthesizes the information and writes the answer.
5. It returns a grounded answer, with the queries it ran and with citations that tie each passage of the text to the URL that backs it.

Google sums up what it is for in three benefits: more accuracy, because it **reduces [hallucinations](/glossary/ai-hallucination/)** by basing the answer on real information; access to recent information; and citations that show where the model's claims come from.[^gemini-grounding] Reducing is not eliminating, and it is worth reading it that way.

For a store, three of those steps matter: that the model decides to search, which queries it writes and which pages it ends up citing. The first does not depend on you. The third does, in part: in Google Search, the pages that get cited come from its index, retrieved with the same systems that order the usual results.[^google-ai-guide]

## Who decides whether your pages serve as a source

At Google there are two paths, and they are controlled separately. [Google-Extended](/glossary/google-extended/) is the name Google lets you use, from [`robots.txt`](/glossary/robots-txt/), to decide whether the content it crawls from your site can be used to train the next generations of Gemini models and for grounding in the Gemini apps and in the Grounding with Google Search service of Vertex AI. **It does not affect your site's inclusion in Search and it is not a ranking signal**.[^google-crawlers] To show up as a supporting link in [AI Overviews](/glossary/ai-overviews/) or in [AI Mode](/glossary/google-ai-mode/), what counts are Search's rules: the page has to be indexed and able to be shown with a snippet.[^google-ai-features]

At Bing, the word already appears in the tools for publishers. Since February 10, 2026, the AI Performance report in [Bing Webmaster Tools](/glossary/bing-webmaster-tools/) shows grounding queries: the key phrases the AI used when it retrieved the content that ended up cited in its answers. Microsoft warns that the data is **a sample of citation activity, not the total**.[^bing-ai-performance]

## In a store

**Fictitious example.** A customer asks an assistant which lightweight travel tripod can hold a DSLR with a telephoto lens. The same question is resolved in two ways depending on whether the assistant searches or not:

|                                                             | Without grounding                              | With grounding                                                                            |
| ----------------------------------------------------------- | ---------------------------------------------- | ----------------------------------------------------------------------------------------- |
| Where the answer comes from                                 | What the model learned in training[^rag-paper] | Pages retrieved at the moment of the question[^google-crawlers]                           |
| [Price and availability](/glossary/price-and-availability/) | Those from the training date, if it knows them | Whatever the retrieved page says                                                          |
| Sources                                                     | None specific to link                          | Citations that tie each passage to a URL[^gemini-grounding]                               |
| What your product page needs                                | To have been among the training data           | To be in the index being queried and able to be shown with a snippet[^google-ai-features] |

The right-hand column is the one a store can work on. What the assistant cites from your product page will be what the page says: if the maximum load or the folded weight is not written on the page, the assistant cannot take it from there.


Believing that blocking Google-Extended takes your store out of AI Overviews or AI Mode. Google-Extended limits Gemini training and grounding in the Gemini apps and in Vertex AI, but **it does not affect inclusion in Search**.[^google-crawlers] For Search's AI features, what counts is that the page is indexed and can be shown with a snippet.[^google-ai-features]


## How to check it

1. **Ask an assistant with search a purchase question from your category**, without your brand, and note whether it cites sources and which. Repeat it another day: a single answer is a sample of one.
2. **Open each cited page and look for the sentence.** The citation says where the information came from; that grounding reduces hallucinations does not mean it eliminates them.[^gemini-grounding]
3. **Look in `robots.txt` at what you tell Google-Extended** and decide knowing what it controls: training and grounding in Gemini and Vertex AI, not Search.[^google-crawlers]
4. **If you have your site in Bing Webmaster Tools, open AI Performance** and read the grounding queries that led to your pages being cited.[^bing-ai-performance]
5. **Confirm in [Search Console](/glossary/google-search-console/) that your product pages are indexed** and that nothing keeps them from showing a snippet, the condition for serving as a supporting link in Google's AI features.[^google-ai-features]

## Related concept: RAG

RAG and grounding are used as synonyms, and Google does so in its own guide.[^google-ai-guide] The useful difference is one of scope. **RAG names an architecture**: a retriever that looks up documents in an index and a generator that writes the answer with them, as in the 2020 paper.[^rag-paper] Grounding names the goal, that the answer rests on an external source, and that source can be a search engine's index, as in the Gemini API or in Bing's answers.[^gemini-grounding][^bing-ai-performance] Put another way, RAG is one way of doing grounding, and grounding with a search engine is the kind that can end up citing your store.

To go on, the [GEO (Generative Engine Optimization)](/glossary/geo/) entry explains how to work so a [generative engine](/glossary/generative-engine/) cites your store, and the [AI search reading path](/learn/ai-search/) puts the rest of the terms in order.