Definition
Grounding is the process of tying an AI model's response to specific information retrieved when the question is asked, such as web pages, documents or a database. Instead of relying only on what it learned during training, the model reads the retrieved material and bases its answer on it, often citing the sources used.
For example, when a user asks Perplexity about Sunnara's newest sunscreen, the system searches the web, retrieves Sunnara's product page and a recent review, and writes its answer from those pages. The answer is grounded because it can point to where each claim came from.
Why it matters
Grounding is how current pages can influence AI answers without waiting for a model to be retrained. It is also where citations come from. For brands, this means accurate, up-to-date and accessible pages have a real chance to shape what assistants say, and outdated pages can spread outdated claims. Grounding reduces some errors, but models can still misread or misquote a source, so answers are worth checking.
What to do
- Keep key facts, such as pricing and features, current on crawlable pages.
- Check grounded answers against the cited sources to see if you were represented correctly.
- Update or correct third-party pages that assistants retrieve with outdated information about you.
Common questions
What is grounding in AI?
Grounding means basing a model's response on information retrieved at answer time, such as search results or documents, rather than on training data alone. Grounded answers usually include citations. It is commonly implemented through retrieval-augmented generation, where the system searches first and then writes from what it found.
Does grounding stop AI hallucinations?
It reduces some of them, because the model has source material to work from. It does not eliminate them. A model can still misread a source, combine facts incorrectly or rely on an inaccurate page. Checking answers against their cited sources remains necessary, especially for claims about your brand.