What is LLM Visibility?
- LLM Visibility
- LLM visibility is the extent to which large language models know a brand exists, associate it with a category, and name it when answering related questions. LLM visibility has two sources: what a model absorbed about the brand during training, and what a model can retrieve about it live while composing an answer. A brand can be strong on one and absent on the other, and the two are repaired differently.
01 In practice
The training half of LLM visibility is built off-site and slowly. A model associates a brand with a category because enough was written about that brand, across enough sources the model absorbed, before its training cut-off, and nothing published today changes what a released model already learned.
The retrieval half moves faster and is mostly mechanical. Answer-time agents have to be allowed to fetch the page, the content has to be present in the server response, and a passage has to survive being lifted out of the page around it. Fixing those changes what a model can use in the next answer rather than in the next model.
Diagnosis starts by separating the two halves. A brand named when the assistant browses and absent when it answers from memory has an authority problem off-site; a brand named from memory and missed during retrieval usually has a crawler or rendering problem on its own domain.
02 Related terms
The terms next to this one.
Core
Grounding
Tying a generated answer to sources it can be traced back to.
Technical
Retrieval
A model fetching live pages while composing an answer.
Measurement
Share of Answer
How often an assistant names you, against how often it names rivals.
Core
Generative Engine Optimisation (GEO)
Making a brand appear in the answers AI assistants write.
The work behind it
Rank on Google. Get cited by ChatGPT.
The sources assistants quote and the pages search still ranks, one engagement, both surfaces.