AI grounding

Grounding is tying the answer to retrieved evidence so it hallucinates less. Citations are the visible part.

Ungrounded answers are fluent memory. Grounded answers can still pick the wrong doc.

Your job is to be the doc worth grounding on.

Examples

Perplexity shows footnotes. A model with browsing off does not. Same question, different grounding.

A hallucination is a confident falsehood: fake URLs, fake papers, fake quotes. GEO programs must log them, not only “wins.”

Share of answers that attach retrieved sources versus pure memory. Engine-level stat, useful context for citation rate.

RAG means the model looks up documents at answer time instead of relying only on what it memorized in training.

Does grounding guarantee truth? +

No. It guarantees a source was nearby. The source can be wrong or outdated.

See Reddit threads and AI answers for your brand in one place. Start with a free analysis.

Get started