Temperature Explorer
Understand how temperature controls randomness in LLM outputs — the most important sampling parameter.
Next-token probability distribution — Prompt: "The neural network learned to"
At temperature 0, the model always picks "recognize" (highest logit).
As temperature rises, other tokens become viable.
Sample outputs — Prompt: "Describe the night sky"
The Math: Softmax with Temperature
When to use each temperature range