Large language models (LLMs) generate text by predicting sequences of tokens—words or parts of words—based on patterns learned during training. When given an input prompt, the model processes the text token by token, using its internal neural network to estimate the probability of each possible next token. It then selects a token (either the most likely or a randomized choice) and repeats the process, appending each new token to the input to generate a coherent output. This autoregressive approach allows the model to build longer responses incrementally.
The core architecture enabling this process is the transformer, which uses self-attention mechanisms to weigh the relevance of different parts of the input text. For example, when generating the sentence “The cat sat on the mat,” the model might first process “The cat sat on the” and compute attention scores to determine that “mat” is a likely next word, based on patterns seen in training data like “cat” often being associated with “mat.” During training, LLMs optimize their parameters to minimize prediction errors across vast datasets, learning grammar, facts, and contextual relationships. For instance, if the input is "2 + 2 =", the model learns to predict “4” by recognizing numerical patterns in math-related texts.
Text generation also depends on decoding strategies and parameters that control randomness and diversity. For example, “greedy decoding” selects the highest-probability token at each step, which can lead to repetitive outputs. In contrast, “temperature” scaling adjusts the randomness: a low temperature (e.g., 0.2) makes the model favor high-probability tokens (producing safer, predictable text), while a high temperature (e.g., 1.0) allows more varied choices. Developers might also use “top-k sampling,” which limits the model to selecting from the top k most likely tokens. These settings let developers balance creativity and coherence—for instance, a chatbot might use a moderate temperature to avoid sounding robotic while staying on-topic.