Temperature in AI, particularly in language models, is a parameter that controls the randomness or "creativity" of the model's outputs. Let me explain how it works: Temperature is typically a value between 0 and 1 (though some systems use different scales). Here's how different temperature settings affect output: At temperature = 0:
The model always selects the most probable next token/word Outputs are deterministic and consistent Good for tasks requiring accuracy like factual Q&A or coding Less creative, more focused responses
At higher temperatures (0.5-1.0):
The model samples from a broader range of possible next tokens Outputs become more diverse and unpredictable Better for creative writing, brainstorming, or generating variations Can sometimes lead to more errors or less coherent text
Think of it like this: At temperature 0, the model always picks the "safest" choice - like always ordering your usual at a restaurant. At higher temperatures, it's more like rolling weighted dice - less common but still reasonable choices have a chance of being selected. The choice of temperature depends on your use case. If you're writing code or need factual accuracy, you'd want a low temperature. For creative writing or generating multiple ideas, you might want a higher temperature.
