In plain words
A chatbot on its own answers from memory: patterns it picked up in training. That memory can be out of date, and it can be wrong while sounding sure. That is a hallucination.
Grounding gives the AI sources to answer from. Before it replies, the system fetches relevant material, such as fresh search results, a company's policy files or the PDF you uploaded, and tells the model to stick to what those sources say.
A grounded answer usually comes with citations: small links or numbers that point to the exact page or passage behind each claim. You can click and check. That is the big benefit. Grounding makes answers more current and more checkable.
It does not make them automatically true. The sources can be wrong, and the AI can still misread or overstate them.
Everyday example
An open-book exam
In a closed-book exam you write from memory and might misremember a date. In an open-book exam you can look it up and quote the page. Your examiner can then check your reference. Grounding turns the AI's test into an open-book one, and the citations are its page numbers.
Where the example breaks: A student usually reads the page they cite. An AI can attach a citation to a sentence the source does not fully support, so a citation is a place to check, not proof.
How it actually works
Google's documentation defines grounding as the ability to connect model output to verifiable sources of information. There are a few common ways to do it.
Ground in web search
The model decides whether a search would help, writes its own search queries, reads the results and writes an answer with links. This is how AI search engines and many chatbots answer questions about recent events.
Ground in your own documents
For company files or a set of manuals, retrieval-augmented generation finds the most relevant passages, often with semantic search, and places them in the model's context window next to your question.
Attach citations
Some APIs return each claim with a pointer to the exact passage it relies on, such as a character range or page number. Anthropic says its citations always point to real places in the documents provided, though that alone does not prove the claim is right.
Check the support
Grounded answers can still drift. A 2023 Stanford study of four AI search engines found only about half of their sentences were fully supported by the sources they cited. Careful systems check, and careful readers click.
- Main benefit
- Fewer made-up facts, more up-to-date answers, and sources you can check
- Common tools
- Web search, your own files through RAG, maps and databases
- Measured gap
- 2023 audit: 51.5% of AI search sentences fully supported by citations, 74.5% of citations supported their sentence
- Older meaning
- The "symbol grounding problem" (1990): how words in a computer could mean anything without links to the real world
Questions people ask
What is the difference between grounding and RAG?
Grounding is the goal: answers tied to real sources. RAG is one way to get there: retrieve relevant passages from a document collection and give them to the model. Grounding can also use live web search, maps or databases.
Does grounding stop hallucinations?
It reduces them, but does not stop them. The model can still misread a source, mix two sources up, or add details that are not there. Grounding helps most because it gives you citations to check.
What does "grounding with Google Search" mean?
It is a feature in Google's Gemini API. When it is switched on, the model can run Google searches before answering and return the answer with links to the web pages it used.
Is a grounded AI answer always correct?
No. It is only as good as its sources and how faithfully the AI uses them. A citation tells you where to check, not that the claim is right. For anything important, open the source and read the passage.
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