Data
Temperature (AI Parameter)
Temperature is a numerical parameter (typically 0–2) that controls the randomness of an AI model's output. At temperature 0, the model deterministically selects the highest-probability token at each step, producing consistent, conservative outputs. At higher temperatures, lower-probability tokens are sampled more often, producing more varied and creative — but potentially less accurate — outputs.
For enterprise AI deployments, temperature is a tunable reliability dial. Customer-support and document-extraction tasks benefit from low temperature (0–0.3) for consistency. Creative writing and brainstorming tasks use higher temperature (0.7–1.0) for variety. Production systems handling regulated tasks (clinical notes, financial analysis, legal drafting) should default to low temperature and test any increase against accuracy benchmarks before deploying.
Related terms
- LLM (Large Language Model) — A large language model (LLM) is a neural network trained on broad text corpora that can generate, summarize, translate, classify, and reason about natural language.
- Inference — Inference is the process of running a trained AI model to produce outputs from inputs.
- Hallucination — A hallucination is when an AI model generates text that is fluent, confident, and factually wrong.
- Fine-Tuning — Fine-tuning is the process of training an existing AI model on additional task-specific data so its weights adapt to a narrower domain.