What is Grounding?
Grounding — Connecting an AI model’s outputs to verifiable facts or external databases to prevent hallucination.
Grounding connects AI outputs to verifiable external sources. Instead of relying solely on training data, grounded models reference specific documents, databases, or APIs to support their responses. This dramatically reduces hallucinations and enables source attribution.
Frequently Asked Questions
How is grounding different from RAG?
RAG is one method of grounding. Grounding is the broader concept of anchoring AI outputs in verifiable facts. Other grounding methods include web search, knowledge graphs, and database lookups.
Does grounding eliminate hallucinations?
It significantly reduces them but does not eliminate them entirely. The model can still misinterpret or incorrectly synthesize retrieved information.
How do I implement grounding?
The simplest approach is RAG — retrieve relevant documents before generating responses. Google and OpenAI also offer built-in grounding features that automatically search the web for supporting facts.