What This Is
We start with a counterintuitive fact: at Temperature=0, large language models produce fully deterministic outputs for the same prompt—which means "AI answers differ every time" is not its nature, but a feature quietly switched on by default settings.
Temperature is the parameter controlling this "degree of randomness," with values typically ranging from 0–2. It adjusts the model's preference for selecting tokens from the probability distribution: when temperature approaches 0, the model tends to pick the highest-probability token, producing stable but possibly stilted output; when temperature rises, the model takes more chances on less likely tokens, becoming more "creative" but also more likely to derail. ChatGPT, ERNIE Bot, Tongyi Qianwen, and Claude all expose this parameter in their APIs, but consumer apps typically lock it around 0.7 on your behalf—you never see it.
Industry View
The mainstream consensus is: enterprise applications almost default to Temperature=0. "Same input must yield same output" is a prerequisite for compliance, regression testing, and audits. That's why low temperature has become the industry default in code generation, mathematical reasoning, and SQL scenarios.
But dissenting voices exist. Some academic studies show that the "diversity" brought by higher temperature is often noise rather than genuine creativity—just errors wrapped more attractively. Engineers at Anthropic and OpenAI also lean toward using system prompts to control style in public talks, rather than letting temperature run loose.
Impact on Regular People
For enterprise IT: Stop copying the 0.7 from sample code when integrating LLM APIs—first clarify whether your business needs "stability" or "divergence," then standardize a default configuration.
For working professionals: When using AI to write code, build spreadsheets, or run analysis, setting temperature to 0–0.3 noticeably improves accuracy; for marketing copy or brainstorming, raise it to 0.8 or above.
For the consumer market: Consumer AI products don't let you adjust temperature—in essence, they trade "explainability" for a "personalized-for-everyone" experience. When you sense that "AI is sometimes brilliant, sometimes dumb," this curve is usually behind it.