Grounding (LLM)
Grounding is the technique of anchoring a language model's answer in retrieved, verifiable sources — search results, documents, or a knowledge base — rather than in parametric memory alone. Grounded answers cite their sources and track current facts; ungrounded answers rely on training data, so brand information degrades toward the model's knowledge cutoff.
Grounding exists because parametric memory has two structural flaws for factual queries: it goes stale (knowledge cutoff) and it interpolates (hallucination). Feeding the model retrieved documents at answer time addresses both — the model paraphrases evidence instead of reconstructing facts from statistical memory.
The practical implication for brands is that grounded surfaces are fixable surfaces. If Perplexity or Gemini misstates your pricing, the citation trail shows which retrieved page misled it, and correcting that page changes future answers — often within days. Ungrounded surfaces can only be fixed by the slower cycle of model refreshes ingesting better data.
Grounding is not a guarantee of accuracy: retrieval can select a stale or hostile page, and the model can still blend grounded facts with memorized errors. That failure mode — a cited answer that is nonetheless wrong — is exactly what claim-level monitoring is designed to catch.
Related terms
See what AI currently says about your brand — free snapshot.