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Grounding is the process by which a large language model connects its generated text to specific factual sources or retrieved web content. A grounded response is one that references real, verifiable information rather than generating text purely from learned patterns. RAG (Retrieval-Augmented Generation) is the primary technical mechanism for grounding — the LLM retrieves real web pages and uses them as the basis for its answer.

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Why it matters

Grounded responses are more factual, more likely to cite sources, and more trustworthy. For brands, grounding creates an opportunity: if your content is high-quality, well-structured, and accessible to AI crawlers, it can serve as grounding material — the factual basis for the AI’s answer about your category. Ungrounded responses are more prone to hallucination and less likely to cite any source at all.

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How it applies in practice

The platforms Cited tracks vary in how heavily they ground their responses. Perplexity is the most grounded — every response is built on real-time retrieved sources. ChatGPT in search mode is grounded via Bing retrieval. Claude is the least grounded in its standard mode, relying primarily on training data. The degree of grounding directly affects citation rate patterns across platforms.