Grounding
The practice of tying an AI model's response to verifiable external sources, reducing fabrication and allowing the answer to cite where its information came from.
Grounding means anchoring an AI-generated answer in retrieved, verifiable sources rather than letting the model rely solely on memorized patterns. A grounded answer can point to the specific documents that support its claims.
Why it matters
Grounding reduces hallucination and makes answers citable. For brands, it means the accuracy of the sources an engine grounds on directly determines whether the answer about you is correct.
How to support grounding
- Publish clear, factual, verifiable statements about your business
- Keep critical facts (offerings, locations, pricing context) consistent everywhere
- Use structured data to state facts unambiguously
- Correct inaccurate third-party information models might ground on
Practical takeaway
If a model grounds on outdated or wrong information about you, it will confidently produce a wrong answer. Controlling the ground truth is the fix.
Related Terms
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Embeddings
Numerical representations of text that capture meaning, allowing AI systems to match a query to relevant content based on semantic similarity rather than exact keywords.
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Hallucination
When an AI model generates information that is false, fabricated, or unsupported by its sources while presenting it as fact.
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Query Fan-Out
A technique where an AI search system breaks a single user question into many related sub-queries, retrieves results for each, and synthesizes them into one answer.