Why it matters to a buyer
Understanding that an LLM's core knowledge is frozen at a training cutoff, and only updated live through grounding when the product supports it, explains why a business can be well described in one assistant and inaccurately or not at all in another: it depends on whether that specific model was trained after the business became notable, and whether the assistant is grounding its answer in a live search at all. A buyer should ask which route, training memory or live retrieval, a given optimisation effort is actually meant to influence.
How it is measured or applied
An LLM generates text by predicting the most statistically likely next words given its input and training, which is why it can produce fluent, confident answers that are nonetheless factually wrong when it lacks reliable grounding. Influencing how a specific model describes a brand from memory alone is slow and indirect, since it requires being described accurately and repeatedly across content likely to have been part of a future training run. Influencing what a model retrieves live is faster, achieved through the same crawlability and content clarity that supports any AI citation, and is measured the same way, through tracked prompts run on a schedule.
Sources
- What is a large language model (LLM)?, Google Cloud
