Listen: Temperature
A setting that rescales next-word probabilities to control randomness — low values make output focused, high values more diverse.
Transcript
When you interact with an artificial intelligence model, the temperature setting acts as a dial for creativity. It controls how random or predictable the AI's response will be by shifting the mathematical probabilities of the words it chooses next.
At a low temperature, between point-one and point-seven, the model plays it safe. It sharpens the probability distribution, making the most likely words even more dominant. The resulting text is focused, conservative, and highly repeatable. If you turn the temperature all the way down to near zero, the model always picks the single most probable word, leaving no room for surprise.
When you turn the temperature up, above point-eight, the model flattens those probabilities. This gives less common words a real chance to be selected. The output becomes more diverse, creative, and unexpected, though you run the risk of the text becoming incoherent. A temperature of exactly one leaves the model's learned probabilities completely unchanged.
For anyone tracking how a brand or concept is represented by AI, temperature is the key. It explains why the exact same prompt can yield a completely different description from one run to the next. By shaping token probabilities, the temperature setting ultimately decides whether the AI sticks to the script or wanders into more creative territory.