Grounding.
Learn what Grounding means in modern AI and large language models.
The practice of anchoring a language model's output to verified external sources rather than relying solely on what the model "remembers" from training.
Grounding solves one of the LLM's biggest problems: confident-sounding answers based on training data that may be outdated, sparse, or simply wrong. A grounded system uses retrieval (RAG, web search, internal docs) to fetch fresh, attributable facts and then forces the model to cite them.
For brands, grounding is the mechanism that makes AI engines cite you. If your content is structured and retrievable, you become part of the grounding sources for queries about your topic. If it isn't, the model falls back to general knowledge and you're invisible.
Articles about Grounding
Read more on the Aergos blog.
Ready to close the loop?
See every term in action
Aergos tracks your AI and organic visibility across every channel, in one platform.
Not ready to talk? Audit your site free →