An LLM is a model trained on very large text corpora that generates text conditioned on context. It is the technology behind ChatGPT, Claude, Gemini and Perplexity.
What matters for GEO is that an LLM draws on two separate channels: training data (static, historical) and live search (current). Brand visibility is earned differently in each — the first through years of entity building, the second through weeks of technical and content work.
Model output is probabilistic: the same question can produce different answers at different times. A single measurement is therefore an observation, not a result.
A model is not a search engine
A language model generates text; a search engine finds documents. Products like ChatGPT, Perplexity and Gemini combine the two: the model writes the answer, the search layer supplies the sources.
The distinction is practical for GEO. You cannot reach the model, but you can reach the search layer. If your page is findable there, it has a chance of entering the answer. If it is not, no amount of model quality will make it aware of you.
Why the training cutoff matters
Every model has a date at which its training data ends. Information after that date is not in its memory and can only arrive through live search. This is why a new brand or a new product can appear in answers only when grounding is active.
Keep this in mind when measuring: an answer produced with search turned off says nothing about whether your content work succeeded or failed.
Frequently asked
Should I want my content in the training data?
That is a separate decision. Training means long-term memory but it is uncontrollable and brings no citation. It is not required for search visibility; blocking GPTBot while allowing OAI-SearchBot is a coherent position.
Can I reduce the risk of hallucination about my brand?
Partly. Publishing clear, consistent, easy-to-find information reduces the model's need to fill gaps. Inconsistent information does the opposite.
Which model matters most to appear in?
It depends on your audience. ChatGPT dominates in many markets; Perplexity and Copilot also feature in B2B buying. The right answer is to measure your own question set across several engines and see the difference.
