Temperature (AI)
Simple Definition
Temperature is a setting that controls how random or creative an AI model’s responses are.
- Low temperature (close to 0) — responses are more focused, consistent, and predictable
- High temperature (close to 1 or above) — responses are more varied, creative, and sometimes surprising
Think of it like a dial between “safe and precise” and “wild and creative.”
How Temperature Works
When an AI generates text, it assigns probabilities to potential next tokens (words or word-pieces). Temperature adjusts how those probabilities are used:
- Low temperature — strongly favors the most likely next token. The output is predictable and consistent.
- High temperature — spreads probability across more options. The output is more varied but may be less coherent.
Practical Guide
| Temperature | Best For |
|---|---|
| 0 | Factual Q&A, data extraction, classification |
| 0.3–0.5 | Technical writing, structured summaries |
| 0.7–1.0 | Creative writing, brainstorming, marketing copy |
| 1.0+ | Experimental or deliberately surprising output |
Most general-purpose AI tools default to around 0.7.
When You Can Set It
Consumer tools like ChatGPT don’t expose temperature as a user setting. You typically control it when using:
- The OpenAI, Anthropic, or Google AI APIs directly
- AI coding environments like LangChain or LlamaIndex
- Developer tools and custom AI applications
Related Terms
- LLM — the models temperature applies to
- Inference — the process where temperature affects output
- Prompt Engineering — working with model settings to get better outputs
- Token — the unit of text temperature affects at each generation step
See AI terms in action
Browse practical AI workflows that use the concepts in this glossary.
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