Glossary
LLM visibility
LLM visibility is how present a brand is inside the outputs of large language models, both in their direct responses and in the sources they retrieve when answering. It spans the models behind tools like ChatGPT, Claude and Gemini.
LLM visibility has two layers. One is what a model says from its trained parameters when asked without live search. The other is what it surfaces when it retrieves from the live web. A brand can be known to a model yet rarely retrieved, or retrieved often yet described inconsistently.
Because model knowledge and live retrieval can diverge, improving LLM visibility means working on both the entity signals that shape what a model knows and the page signals that shape what it retrieves and cites.
Common questions
How is LLM visibility different from AI search visibility?
AI search visibility centers on AI-powered search surfaces and their live answers. LLM visibility also includes what a model returns from its trained knowledge without searching. They overlap on retrieval-based answers.
Can you change what a model already knows?
Not directly, and not quickly. You influence it over time through consistent entity signals, authoritative profiles and content that is retrieved and cited, which feeds future training and live retrieval.