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Technical implementation · AI Search Infrastructure

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Definition

Model grounding is the practice of connecting an AI model’s outputs to specific, verifiable external data sources — either through retrieval-augmented generation, tool use, or real-time web access — to ensure responses are factually anchored rather than generated purely from training data. Model grounding is the mechanism that makes AI search different from AI chat. A grounded model cites sources because it is retrieving and referencing them — not because it is generating plausible-sounding text from memory. For brands, the practical implication is that grounded AI systems are actively looking for content to retrieve, which means the same structural and entity optimization principles that support RAG-based retrieval also support grounded model outputs.

Grounding

RAG

Retrieval pipeline

Hallucination mitigation

Citation signal

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