Grounding
Grounding is the practice of connecting an AI model to real, authoritative data so its responses are based on actual facts rather than only its training.
It links the model's general knowledge to a specific business's data, improving accuracy and relevance. A model that is not grounded answers from patterns it learned, which can be outdated or wrong for a given company; grounding supplies it with current, trusted context, such as customer records, documents, or metrics, so it responds based on what is true for that business.
Techniques include retrieval of relevant documents, connections to live systems, and a semantic layer that defines terms correctly. Grounding is one of the main ways to reduce hallucination and make AI dependable in enterprise settings.
Frequently Asked Questions
It means connecting a model to real, authoritative data so its answers are based on verified facts rather than only its training.