How engines work
Large language model
Also: LLM
A large language model is a system trained on very large amounts of text to predict and generate language. It is the component that writes an answer engine's response, drawing on what it learned in training plus anything retrieved at the moment of asking.
In more detail
Two properties matter for brand visibility: what the model absorbed during training, and what it can retrieve now. Training shapes its default picture of your category; retrieval is how new information reaches an answer quickly.
Guides that cover this
Related terms
Training data
Training data is the body of text a model learned from. It sets the model's default understanding of a category and which brands it associates with what, before any live retrieval happens.
Knowledge cutoff
A knowledge cutoff is the date after which a model's training data ends. Anything that changed after that date reaches an answer only if the engine retrieves it live at the time of asking.
Retrieval-augmented generation
Retrieval-augmented generation is when a model searches for relevant documents and writes its answer from what it finds, rather than from training alone. It is why a page published this week can appear in an answer today.
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