A large language model generates text by estimating which token is likely to come next given the context. Its fluency comes from learning patterns across large training datasets, not from human-like understanding.
Tokens and probabilities
Text is split into tokens, which may be words, word parts or punctuation. The model calculates a distribution of possible next tokens and selects from it. Small changes in context can change the result.
Training and prompting
During training, the model adjusts internal parameters to make predictions closer to examples in its data. A prompt then provides temporary context for a response; it does not automatically make the model an expert on every subject.
Why errors sound confident
The model is optimized to produce plausible sequences, not to guarantee truth. It can invent sources, mix up dates or follow an incorrect premise. Important claims should be checked against primary sources or trusted references.
Useful boundaries
Language models can help draft, summarize, classify and explore ideas. Avoid placing secrets or personal data into tools without understanding their data handling, and keep a human responsible for high-impact decisions.