AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Grounding is tying an AI model's answers to verified sources, such as your own documents, so responses can be traced and trusted. Grounding is the main defense against hallucination. A grounded answer can show its receipts, which is what makes AI usable for compliance-sensitive work.
Grounding is what separates a cited encyclopedia entry from a rumor. The encyclopedia states the fact and shows you its source. The rumor sounds just as confident.
A compliance assistant grounded in the company's actual policy documents cites the exact clause and document version for every answer. An ungrounded model produces an answer that sounds right but may not match current policy.
For any task where accuracy is non-negotiable and a source must back the answer: legal, compliance, medical, financial, and policy-sensitive work.
Grounding requires good source documents. If the underlying knowledge base is incomplete or outdated, grounding amplifies that gap. Maintain the source content as rigorously as the AI system itself.
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