Temperature

Temperature is a crucial hyperparameter that controls the randomness and creativity of an LLM's output. It fundamentally affects how the model selects the next token when generating text, making it one of the most important settings for tuning model behavior.

Technically, temperature modifies the probability distribution over possible next tokens. Here's how different values affect output:

• Temperature = 0: Deterministic output. The model always picks the highest-probability token, resulting in identical responses to the same prompt. Ideal for tasks requiring consistency: code generation, data extraction, mathematical reasoning.
• Temperature = 0.3-0.7: Balanced creativity. Introduces some variation while maintaining coherence. This range works well for most business applications like customer support, technical writing, and analysis.
• Temperature = 1.0: Default/neutral sampling from the model's natural distribution. Provides good variety without becoming erratic.
• Temperature = 1.5-2.0: High creativity and randomness. The model takes more risks, producing unexpected and diverse outputs. Useful for brainstorming, creative writing, or generating multiple varied responses. However, outputs may become incoherent or nonsensical.

Temperature interacts with other sampling parameters like top-p (nucleus sampling) and top-k. Lower temperature narrows the model's "focus," while higher temperature expands its exploratory range. Understanding temperature is essential for optimizing LLM applications because the same model can behave completely differently based on this single parameter.

As a best practice, start with lower temperatures (0.0-0.3) for factual, deterministic tasks, and gradually increase for creative applications while monitoring output quality.