Large Language Model (LLM)
A large language model is a neural network trained on vast amounts of text to predict the next piece of language, and it is the model type behind systems like ChatGPT, Claude, and Gemini, generating fluent answers one token at a time rather than retrieving stored facts.
An LLM does not look up answers in a database the way a search index does. It predicts the most likely continuation of the text in front of it, based on statistical patterns learned during training. That is why the same question can yield slightly different wording each time, and why an LLM can sound confident about something it was never reliably taught.
This prediction-not-retrieval design matters for whether a product or brand gets mentioned. An LLM tends to surface entities, claims, and language that appeared often and consistently across its training data and any sources it is given at answer time. Sparse, contradictory, or unverifiable information is easy for the model to skip or get wrong. Getting existing reviews readable, corroborated, and citable by these systems is the gap BetterReviews closes.
The honest caveat: an LLM has no built-in notion of truth, only of likelihood, so it can produce fluent but false statements, a failure mode known as hallucination. Many products now pair the model with retrieval over trusted sources to ground its answers, which reduces but does not eliminate the problem.