Grounding.
Learn what Grounding means in modern AI and large language models.
The practice of anchoring a language model's output to verified external sources rather than relying solely on what the model "remembers" from training.
Grounding solves one of the LLM's biggest problems: confident-sounding answers based on training data that may be outdated, sparse, or simply wrong. A grounded system uses retrieval (RAG, web search, internal docs) to fetch fresh, attributable facts and then forces the model to cite them.
For brands, grounding is the mechanism that makes AI engines cite you. If your content is structured and retrievable, you become part of the grounding sources for queries about your topic. If it isn't, the model falls back to general knowledge and you're invisible.
Articles about Grounding
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Google's Deep Dive on PDFs: Ranking, Crawling, and AI Visibility
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AI Hallucinations in Marketing: What They Are and How to Catch Them
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Why Asking ChatGPT for SEO Advice Often Gives You Outdated Answers
ChatGPT can write a brief, brainstorm keywords, and explain concepts in plain English. But ask it for SEO strategy and you might get advice that stopped working years ago. Here's why that happens and what to do instead.
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