What is Temperature?
Temperature is a parameter in language model inference that controls the randomness of text generation - lower values produce more predictable, conservative outputs, while higher values produce more creative, diverse, and unpredictable outputs.
Temperature Explained
Temperature is one of the most important parameters you can adjust when working with large language models. It controls the randomness in the model's output by adjusting the probability distribution over possible next tokens. Understanding temperature helps you tune AI outputs to match your specific use case.
Technically, temperature scales the logits (raw scores) that a model assigns to each possible next token before sampling. A temperature of 1.0 uses the model's learned probabilities as-is. A low temperature (0.1-0.5) makes the distribution more peaked, concentrating probability on the most likely tokens and making the model more deterministic and conservative. A temperature of 0 always picks the single most likely token, making the model fully deterministic. A high temperature (1.5-2.0) flattens the distribution, giving lower-probability tokens more chance of being selected, making the model more creative and unpredictable.
The right temperature depends on your task. For factual tasks like question answering, code generation, or data extraction, a low temperature (0.0-0.3) is best - you want reliable, accurate, consistent outputs. For creative tasks like brainstorming, story writing, or generating diverse options, a higher temperature (0.7-1.2) is better - it produces more varied and surprising outputs. Most general-purpose applications use a temperature around 0.7-1.0 as a balanced default.
Temperature interacts with other sampling parameters like top-p (nucleus sampling), which restricts sampling to only the tokens whose cumulative probability reaches a threshold, and top-k, which restricts sampling to the k most probable tokens. Together, these parameters give fine-grained control over the character of model outputs.
When using AI writing copilots or other AI tools, you may not directly control temperature - the application developers have typically set appropriate defaults for the use case. But understanding temperature helps you interpret why AI outputs sometimes feel repetitive (low temperature) or go off in unexpected directions (high temperature), and what you might request when you want to adjust the style of AI assistance.
Key Takeaways
Where is Temperature Used?
Configuring language model inference for tasks ranging from deterministic code generation (low temperature) to creative writing (high temperature).
How Copilotly Uses Temperature
Copilotly tunes temperature per specialist instead of exposing a confusing slider: the Legal Copilot runs cold for precise, repeatable clause analysis, while the Creative Writing Copilot runs warm to offer genuinely varied phrasings. The right randomness level is part of what makes each of the 131 copilots feel purpose-built.
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Frequently Asked Questions
What is the difference between temperature and top-p sampling?+
Temperature rescales the entire probability distribution over next tokens, flattening or sharpening it, while top-p (nucleus sampling) truncates it, keeping only the smallest set of tokens whose probabilities sum to p. Temperature changes how adventurous every choice is; top-p caps how far into the unlikely tail the model may reach. They are often combined.
What does temperature 0 actually do?+
At temperature 0 the model always picks the single highest-probability token, making output nearly deterministic: the same prompt yields essentially the same answer. It is the standard choice for extraction, classification, and code where consistency beats variety, though minor nondeterminism can still arise from hardware-level computation.
What temperature should you use for different tasks?+
Common practice: 0 to 0.3 for factual Q&A, data extraction, and code; around 0.7 for general conversation and drafting; 0.9 to 1.2 for brainstorming and creative fiction. Above roughly 1.5 most models degrade into incoherence, so high settings need tight prompts.
Does higher temperature make a model more intelligent or more creative?+
Neither, strictly: it only changes how the model samples from probabilities it already computed. High temperature surfaces less likely continuations, which reads as creativity, but it equally surfaces errors. The model's knowledge and reasoning are unchanged; only the dice being rolled differ.
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