Why it matters

LLMs are the technology behind the answers your buyers now read. Understanding how they work — tokenisation, context windows, retrieval, generation — explains why GEO behaves differently from SEO and why some content gets cited while other content gets paraphrased away.

How it relates to GEO

Every GEO technique is an intervention on the pipeline an LLM uses to answer: the crawler that collects your pages, the retrieval step that finds them, and the generation step that decides whether to cite you. The more your content matches how the model retrieves and reasons, the better your outcomes.

Practical implications

You do not need to be a machine learning engineer, but you do need model literacy: know the difference between a model’s training knowledge and its retrieval of live sources, and understand that answers change as models update. GEO programmes must be re-validated after model releases.

Examples

A brand appears in ChatGPT answers consistently until a new model version changes retrieval behaviour and shifts which sources get cited. Teams that monitor citation rates per platform catch the change in days; teams that do not, discover it in lost pipeline.

LLMs depend on tokenisation and fine-tuning, and are prone to hallucination; each answer engine applies them differently.

Frequently asked questions

Does an LLM know my website? Only if it was in training data or is retrieved live through search — most current answers mix both, which is why retrievability work matters.

Do I need to optimise for every model? No — focus on the engines your market actually uses; the platform guides compare behaviours.