AI grounding is the practice of connecting AI-generated responses to verified source information—knowledge bases, policies, customer data—rather than relying on the model's training data alone.

Grounding is the primary defense against hallucination. An ungrounded AI generates responses from its parametric knowledge—what it learned during training, which may be outdated, incomplete, or wrong for your specific business. A grounded AI generates responses based on retrieved, verified information that you control.

The most common grounding architecture is retrieval-augmented generation (RAG), where relevant documents are retrieved and provided to the model as context. But grounding extends beyond document retrieval: grounding the AI in real-time customer data, in current policy versions, in live system states. Each grounding source adds accuracy for its domain.

Grounding is necessary but not sufficient for accuracy. The AI can still misinterpret retrieved information, fail to surface the right documents, or confuse context when multiple sources conflict. Grounding reduces hallucination risk; monitoring and guardrails address the remaining risk.

Related terms: Retrieval augmented generation, Knowledge base, Hallucination detection