What temperature should I use?

Start with 0.7 for general use. Lower (0-0.3) for factual tasks, code generation, and data extraction. Higher (0.8-1.0) for creative writing and brainstorming. Temperature 0 ensures reproducible outputs.

What is the difference between temperature and top_p?

Both control randomness but differently. Temperature scales probabilities before sampling. Top_p (nucleus sampling) only considers tokens whose cumulative probability reaches p. Usually adjust one, not both.

Can temperature cause hallucinations?

Higher temperatures increase randomness, which can lead to more unexpected outputs but not necessarily more hallucinations. Hallucinations are more related to knowledge gaps than temperature. Low temperature doesn't prevent hallucinations.

Why would I use temperature 0?

Temperature 0 produces deterministic outputs - same input always gives same output. Useful for testing, debugging, data extraction, and any task where consistency and reproducibility matter more than variation.

Book Free Assessment Calculator

Temperature

A parameter controlling randomness in AI outputs. Lower temperature (0-0.3) gives consistent, focused responses; higher (0.7-1.0) gives more creative, varied ones.

In-Depth Explanation

Temperature is a parameter that controls the randomness of AI model outputs. It affects how the model selects from possible next tokens - lower temperatures make it more deterministic, higher temperatures more creative.

How temperature works:

Model predicts probability distribution over possible next tokens
Temperature scales these probabilities before sampling
Low temperature sharpens the distribution (top choice more likely)
High temperature flattens it (more tokens become viable)

Temperature settings:

0: Deterministic - always picks highest probability token
0.1-0.3: Very focused, consistent, good for factual tasks
0.5-0.7: Balanced, good default for most tasks
0.8-1.0: Creative, varied, good for brainstorming
>1.0: Very random, may become incoherent

Temperature recommendations by task:

Data extraction: 0
Q&A, factual: 0.1-0.3
General assistant: 0.5-0.7
Creative writing: 0.7-0.9
Brainstorming: 0.8-1.0

Note: Temperature interacts with other parameters like top_p (nucleus sampling). Usually, adjust one or the other, not both.

Business Context

Use low temperature for factual tasks like data extraction; higher for creative tasks like marketing copy. Default 0.7 works for most cases.

How Clever Ops Uses This

We tune temperature settings for each use case with our Australian clients. Factual customer support gets low temperature; content generation gets higher values for more engaging outputs.

Example Use Case

"Temperature 0 for invoice data extraction where consistency is critical; temperature 0.8 for brainstorming marketing taglines where variety is valued."

Frequently Asked Questions

Related Terms

Inference Completion

Learn More

Prompt Engineering Best Practices: Master the Art of AI Communication

Learn proven techniques for writing effective prompts that consistently produce high-quality results from LLMs. Includes practical examples, templates, and testing strategies for production applications.

Read article

Tax Compliance Terraform