What is grounding in AI? Definition and how it works

Grounding is the practice of anchoring an AI model’s answer in specific, verifiable sources supplied at the moment it answers, such as web search results or company documents, so that its claims can be traced back to evidence instead of coming only from what the model memorized during training.

When an AI search engine shows numbered sources under its answer, you are looking at grounding. For brands, it decides whose pages get cited and which facts about you the answer repeats.

Grounding vs retrieval-augmented generation

The two terms are often used as synonyms, but they describe different things. Retrieval-augmented generation (RAG) is a technique: fetch documents, then generate. Grounding is the property you want from it: an answer whose statements are supported by those documents. A system can retrieve sources and still write sentences they do not support, which is a failure of grounding even though RAG ran.

You will also see the word in product names. Google, for example, calls its option for connecting a model to search results “Grounding with Google Search”.

How grounding works in AI search

1. The engine decides whether to look things up. Some engines search for almost every question, others only when the question seems to need current or specific information.
2. It retrieves sources. The engine runs one or more searches and keeps the most relevant passages.
3. It instructs the model to stick to them. The passages are given to the model with instructions to base the answer on them and attribute what it uses.
4. It shows the evidence. Citations appear next to sentences or in a list of sources.

Grounding is rarely all or nothing. In the same answer, one sentence may come from a cited page and the next from the model’s general knowledge.

Why grounding changes which sources get cited

An ungrounded answer reflects the model’s training data: what the web said about your category up to its knowledge cutoff, weighted by how often it was said. A grounded answer reflects the pages the engine retrieves today for the searches it runs. That shift has practical consequences:

• Current pages beat old reputation. A brand that dominated the web years ago can be named from memory but not cited, while a newer brand with strong recent coverage gets the links.
• Search performance carries over. Engines that ground in a search index tend to use pages that are easy to find in that index.
• Third parties shape the answer. Comparisons, reviews and directories are frequent sources. If they describe you poorly, or leave you out, the grounded answer does too.
• The engine’s searches are not the user’s prompt. The engine often reformulates the question into several searches (see query fan-out), so the pages that matter are the ones that rank for those.

How to earn citations in grounded answers

• Write pages that answer one question clearly, with the key fact in the first sentences under a descriptive heading.
• Keep prices, features and availability current, and state them in plain text.
• Make sure the pages can be crawled: the AI crawler checker shows which AI crawlers your site lets in.
• Find the third-party pages the engines already cite for your category and work on being included, accurately, in them.

Common mistakes

• Assuming every answer is grounded. Many answers carry no sources at all and come from memory.
• Only watching your own domain. The sources that describe you are often not yours.
• Reading a citation as an endorsement. Your page may be cited for a single fact in an answer that recommends a competitor.

How Mencoro tracks grounded answers

Mencoro records each AI answer’s text and the sources it returns, for ChatGPT, Perplexity, Google AI Overview and Google AI Mode. ChatGPT is asked with web search on, but the model still decides whether to search and cite: when it answers from what it already knows, there are no sources to record, and mentions in the text are still counted. That lets you compare two separate signals, whether your brand is named (Coverage) and whether your pages are among the sources (Link position). Details are on how Mencoro works. The Google AI Overview tracker applies this to Google, and the free Perplexity rank checker gives you a quick sample.

Related glossary terms

• Retrieval-augmented generation (RAG): the technique that fetches the sources an answer is grounded in.
• AI citations: the sources an AI answer links to.
• Knowledge cutoff: why ungrounded answers can be out of date.
• AI hallucination: what happens when an answer is not supported by any source.