What Is Temperature in Prompt Engineering? A Practical Guide

Quick answer: Temperature is a setting that controls how random a language model's output is. Low values (around 0 to 0.3) give focused, consistent answers. Higher values (0.8 and above) give more varied, creative, and less predictable text.

If you've ever run the same prompt twice and gotten different answers, temperature is a big reason why. This guide explains what temperature does, which values to use for which tasks, and what happens under the hood, so you can tune AI output instead of guessing.

What Is Temperature?

A language model generates text one token (a word or word fragment) at a time. At each step, it assigns a probability to every possible next token. Temperature adjusts how the model chooses from those probabilities:

• Low temperature makes the model favor the most likely tokens, so output is focused and repeatable.
• High temperature gives less likely tokens a better chance, so output is more varied and surprising.

The range depends on the provider. Many APIs, including OpenAI's, accept values from 0 to 2, while others, such as Anthropic's, use 0 to 1. Always check the documentation for the model you're using.

Temperature Ranges at a Glance

Temperature (0–2 scale)	Behavior	Best for
0 – 0.3	Focused, consistent, highly predictable	Summaries, data extraction, code, factual Q&A
0.4 – 0.7	Balanced and moderately creative	Blog posts, emails, product descriptions
0.8 – 1.2	Creative and less predictable	Storytelling, brainstorming, marketing ideas
1.3+	Highly random, can become incoherent	Experimental writing and surreal output

Temperature Example: Same Prompt, Different Settings

Prompt: Write a short sentence about a cat sitting on a windowsill.

The outputs below are illustrative examples of how results typically change. Actual output varies by model.

Temperature = 0

"The cat is sitting on the windowsill."

Very predictable and safe. Best for summarization, structured writing, and factual output.

Temperature = 0.5

"A quiet cat lounges on the windowsill, watching the birds."

Balanced and mildly descriptive. A good default for blogs and product copy.

Temperature = 1.0

"The feline monarch surveyed her kingdom from the sunlit perch."

Imaginative and metaphorical. Well suited to creative writing.

Temperature = 1.5 or higher

"Cat sat sill breeze lizard dreams windows hum cloud blink."

Random and sometimes surreal. Occasionally useful for experimental writing, but rarely for anything you need to be accurate.

Recommended Temperature by Use Case

Use case	Suggested temperature
Legal or technical summaries	0 – 0.2
Code generation	0 – 0.3
Customer support replies	0.2 – 0.5
Product descriptions	0.5 – 0.7
Creative fiction	0.7 – 1.0
Brainstorming ideas	0.8 – 1.2
Experimental poetry	1.2 – 1.5

These are starting points, not rules. Test a few values on your own prompts and compare the results.

How Temperature Works: The Softmax Explanation

Before choosing a token, the model produces a raw score (called a logit) for every option and converts the scores to probabilities with the softmax function. Temperature (T) divides those scores first:

probability(token i) = exp(score_i / T) / sum of exp(score_j / T)

• Low T sharpens the distribution, so one or two tokens dominate.
• High T flattens the distribution, so many tokens become plausible and the model is more likely to pick unusual ones.

How to Set Temperature in an API Call

Most AI APIs expose temperature as a simple parameter. Here is an example using the OpenAI Python library:

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="your-model-name",
    messages=[
        {"role": "user", "content": "Write a short sentence about a cat sitting on a windowsill."}
    ],
    temperature=0.2,
)

print(response.choices[0].message.content)

In chat apps like ChatGPT or Claude, you usually can't set temperature directly, so you steer the tone through your prompt instead (for example, "be concise and factual" or "be playful and imaginative").

Temperature vs. Top-P

Many APIs also offer top_p (nucleus sampling), which limits the model to the smallest set of tokens whose combined probability reaches a threshold. Both settings control randomness, so a common recommendation is to adjust one and leave the other at its default.

Common Temperature Mistakes

• Assuming temperature 0 is perfectly deterministic. It makes output much more consistent, but results can still vary slightly between runs.
• Expecting low temperature to fix hallucinations. It reduces randomness, not factual errors. A model can confidently repeat a wrong answer every time.
• Going too high for factual tasks. Values above about 1.2 often hurt accuracy and coherence.
• Ignoring model limits. Some reasoning models fix the temperature or ignore changes to it, so check the documentation first.
• Blaming temperature for a weak prompt. A clear, specific prompt usually improves output more than any setting.

Frequently Asked Questions

What is the best temperature for ChatGPT or other LLMs?

There isn't one best value. Use 0 to 0.3 for accuracy and consistency, around 0.7 for general writing, and 0.8 to 1.2 for creative work.

What does temperature 0 do?

It makes the model pick the most likely token almost every time, producing the most consistent output possible.

Does higher temperature make AI smarter or more creative?

It makes output more varied, which can feel more creative, but it doesn't make the model smarter. Too high, and quality drops.

Can I change temperature in the ChatGPT or Claude apps?

Usually not. Temperature is typically available through the API or developer tools, so in chat apps you guide the style with your prompt.

Conclusion

Temperature is your creativity dial in prompt engineering. Use low values when you need structure and precision, and raise it when you want imagination and variety. Combine the right setting with a clear, well-written prompt, and you'll get the tone and reliability you need for any task.