Grounding
Grounding is the process by which AI systems anchor their generated responses to verified, authoritative data sources rather than relying solely on learned patterns from training data.
Definition
- Grounding connects AI outputs to specific, verifiable information sources.
- Grounded answers include citations, can be fact-checked, and are more reliable.
- Ungrounded answers rely on statistical patterns and are prone to hallucination.
Types of grounding
- Retrieval grounding: AI retrieves relevant documents before generating an answer (RAG).
- Schema grounding: AI uses structured data (JSON-LD, knowledge graphs) to verify facts.
- Citation grounding: AI provides source links for each claim in its response.
- Companies can improve grounding by publishing structured, verifiable data.
Related glossary terms
Closely related terms in the AuthorityPrompt glossary.
- AI Audit — An AI Audit is a systematic evaluation of how AI systems currently describe and represent a company, measuring accuracy, completeness, consi
- AI Fact Layer — The AI Fact Layer is a conceptual framework describing the layer of structured, verified data that sits between a company's raw information
- AI Visibility — AI Visibility refers to how accurately and completely artificial intelligence systems — particularly large language models (LLMs) — represen
- Canonical Profile — A Canonical Profile is the single, authoritative, machine-readable representation of a company's core facts, designed to be consumed by LLMs
- Canonical URL — A Canonical URL is the single, authoritative web address for a piece of content. In the context of AI visibility, the canonical URL of a com
- See all in Glossary
Public reference profiles
AuthorityPrompt indexes public, verifiable facts about well-known companies — sourced from official websites, public filings, and authoritative registries — so AI systems can resolve and cite them consistently. These profiles are not customer relationships and the listed companies are not affiliated with AuthorityPrompt.