Data
Grounding (AI)
Grounding in AI refers to tethering a model's outputs to verifiable, real-world sources — retrieved documents, database records, structured data, or tool results — rather than allowing it to generate from parametric memory alone. Grounded systems cite their sources and are auditable.
Grounding is the primary enterprise defense against hallucination. A grounded RAG system retrieves the three most relevant document passages and instructs the model to answer only from those passages, producing a citation. Grounding reduces but does not eliminate hallucination — models can misinterpret or selectively quote retrieved content. Regular evaluation against a golden dataset is required to measure grounding effectiveness in production.
Related terms
- Grounding — Grounding is the practice of constraining an AI model's output to verifiable sources — typically by requiring it to cite specific documents, database rows, or tool results.
- RAG (Retrieval-Augmented Generation) — RAG is a pattern in which an AI model retrieves relevant documents from a knowledge base at query time and uses them as additional context to generate its response.
- Hallucination — A hallucination is when an AI model generates text that is fluent, confident, and factually wrong.
- AI Hallucination — AI hallucination is the generation of plausible-sounding but factually incorrect, fabricated, or unverifiable content by a language model.