How LLMs Generate Text: Transformers, Attention, and Token Prediction Explained

Large Language Models (LLMs) like ChatGPT and Gemini generate responses one token at a time rather than all at once, relying on a core architecture called the Transformer. The Transformer consists of an encoder, which interprets the input and relationships between words, and a decoder, which produces the response sequentially. A mechanism called Attention allows the model to identify and prioritize the most relevant words in a sentence when building context. Self-Attention enables every word to reference other words in the same sentence, while Multi-Head Attention examines the sentence from multiple perspectives simultaneously, such as grammar, meaning, and context. Together, these components allow LLMs to understand nuanced language and predict the most appropriate next token until a complete response is formed.