Grounding
The mechanism that lets an AI engine base its answer on real sources rather than only its internal memory.
Definition
Grounding refers to an AI engine anchoring its answer in sources retrieved at the moment of the query (web search, document base, structured data), rather than answering solely from its internal knowledge frozen at training time.
Key takeaways
- Grounding explains why an engine can cite recent information, later than its training cutoff.
- A well-structured source has a higher chance of being used as the basis for an answer.
- Not every engine and every answer mode uses grounding in the same way.
- Grounding directly affects the perceived freshness and reliability of an answer about a brand.
Explanation
Without grounding, a language model answers only from what it learned during training, at a fixed date. With grounding, it can query external sources at the very moment of the question and incorporate those elements into its answer.
This is the mechanism that lets engines such as Perplexity, ChatGPT with browsing, Gemini or Claude with web search cite specific pages, recent announcements or up-to-date data about a company.
For a brand, understanding grounding helps answer a simple but often unclear question: why does this information about us show up, and where does it actually come from?
Go further
How AI engines select and cite their sources
The full article on the mechanism of source selection and citation.
Citation rate
The KPI that measures how often a source is actually cited.
AI Search guide
The starting hub to understand how AI answer engines work.
Agentic browsing
The next step: being chosen as a source is not enough if an AI agent cannot then use the site.
AIglebotBetaAIglebot helps companies track their visibility, reputation and recommendation in ChatGPT, Gemini, Perplexity and Claude.