Grounding

Executive Definition: Grounding — The practice of linking an AI model’s outputs to verifiable external sources or databases to improve accuracy and reduce hallucination.

Grounding anchors an AI’s responses in factual data rather than relying solely on patterns learned during training. By referencing specific documents, APIs, or search results, grounded models provide more accurate information and enable proper source attribution.

Frequently Asked Questions

How is grounding different from RAG?

Retrieval-Augmented Generation (RAG) is a specific technique for implementing grounding. Grounding is the broader concept of anchoring outputs in facts, which can also be achieved via web search, tool use, or knowledge graphs.

Does grounding eliminate hallucinations?

Grounding significantly reduces hallucinations but does not eliminate them entirely. Models can still misinterpret, misquote, or incorrectly synthesize retrieved factual information.

How do I implement grounding?

The most common approach is RAG, where relevant documents are retrieved and provided to the model in its prompt. Many platform providers also offer built-in grounding features using web search or proprietary enterprise search.