A setting that controls how random or predictable a language model's output is, with lower values giving more consistent answers.
When a model generates text, it chooses each next token from a range of likely options. Temperature adjusts how strongly it favours the most probable choices. A low temperature makes the model pick the safest, most likely words, producing focused and repeatable output. A higher temperature spreads the choice across more options, producing more varied and sometimes surprising text.
In practice, a team extracting invoice totals into a spreadsheet would use a low temperature so the same invoice gives the same result every time. A marketing team brainstorming taglines might raise it to get a wider spread of ideas. Temperature does not make a model more knowledgeable or accurate. Very high values can make output incoherent, and some newer models fix or limit the setting.
A setting chosen before training a machine learning model, such as learning rate or number of layers, that shapes how the model learns.
InferenceUsing a trained machine learning model to make predictions or generate outputs on new data.
Large Language ModelAn AI model trained on vast amounts of text to understand and generate human-like language, code, and other content.
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