How LLMs Work: Predicting Text One Token at a Time

Large language models (LLMs) generate text by predicting one token at a time rather than planning a full response in advance. Each token is converted into a numerical vector encoding meaning and position, while an attention mechanism helps the model weigh which earlier words are most relevant. The model then calculates probabilities for every possible next token, with parameters like temperature and top-p sampling influencing which token is ultimately chosen. This process repeats in a loop, with the model referencing thousands of prior tokens within its context window to maintain coherence. Because generation is fundamentally probabilistic prediction rather than fact retrieval, LLMs can produce confident-sounding but incorrect outputs.