Grounding is the process of connecting an AI model's generation to verifiable, real-world data sources (like databases or web search) to prevent hallucinations
Grounding ties model outputs to verifiable sources: retrieval injects authoritative documents into context, prompts require answers drawn from (and cited to) those sources, and verification layers check claim-support before display. Variants include grounding in databases, APIs (live facts), and search results.
Grounding converts plausible text into accountable answers: the difference between a demo and a system enterprises can rely on. With citations, errors become detectable and trust becomes inspectable; ungrounded generation at scale is a liability engine.
RAG is the main grounding technique; grounding is the broader goal: outputs anchored to verifiable sources, whether via retrieval, database lookups, or live APIs, usually with citations.
Faithfulness evals: check claim-by-claim whether output is supported by the provided sources, via human review or calibrated LLM judges, and track the supported-claim rate as a release metric.
No: models can cite sources that don't support the claim. Production systems verify citation-claim alignment, not just citation presence.