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Question 6 of 55

How does the temperature parameter interact with your prompt, and when would you adjust it?

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Temperature does not change what your prompt says. It changes how tightly the model sticks to the reading your prompt makes most likely. Low values sharpen the next-token probability distribution toward the likeliest tokens. High values flatten it, so less likely tokens get a chance.

That is why the two interact. A specific, well-constrained prompt already narrows the distribution, so it stays on task at a higher temperature. A vague prompt at 1.0 drifts.

Typical settings, where the API accepts one:

  • Extraction and classification, JSON and tool calls: 0 to 0.2.
  • Summaries and support replies: 0.3 to 0.7.
  • Brainstorming and drafting varied copy: 0.7 to 1.0.

Temperature 0 is near-deterministic, not guaranteed. Tune either temperature or top_p, not both.

Some newer models take the choice away. The newest Claude models reject a non-default temperature with a 400 error, and Google advises keeping Gemini 3 at its default. There, steer variety through the prompt itself.

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Next-token chances by prompt and temperature
total probability 100 percent
Specific prompt, temp 0.2100 of 100 percent
  • Top token near 100%

A narrow prompt already favors one token. Low temperature sharpens that, so it wins almost every time.

Specific prompt, temp 1.0100 of 100 percent
  • Top token 85%
  • 2nd token 8%
  • 3rd token 4%
  • 4th token 3%

At 1.0 the model uses its own odds with no sharpening. The top token still gets 85 percent, so the output stays on task.

Vague prompt, temp 0.2100 of 100 percent
  • Top token 62%
  • 2nd token 22%
  • 3rd token 10%
  • 4th token 6%

Even at 0.2 the favorite gets only 62 percent. The prompt left several readings open, so other tokens win about 4 in 10.

Vague prompt, temp 1.0100 of 100 percent
  • Top token 32%
  • 2nd token 26%
  • 3rd token 22%
  • 4th token 20%

At 1.0 the model uses its own odds, and no token leads by much. Over many picks, the reply drifts off task.

Temperature only reshapes the odds your prompt already set. A specific prompt keeps one clear winner even at 1.0, while a vague one spreads out and drifts.

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Likely follow-ups

  • Why can two calls at temperature 0 with the same prompt still return different text? Name server-side nondeterminism such as floating-point order and batching, and say tests should check properties, not exact strings.
  • What does top_p do, and why tune it or temperature but not both? top_p keeps the smallest set of tokens that covers probability p. Both reshape one distribution, so changing both makes the effect hard to attribute.
  • How does temperature relate to self-consistency? Self-consistency samples several reasoning paths and votes on the answer. That needs a nonzero temperature, or the samples mostly repeat each other.
  • The model rejects a non-default temperature. How do you get varied outputs for brainstorming? Ask for several distinct options in one call, or give each call a different angle or examples, then dedupe the results.
  • How would you pick the temperature for a new feature instead of guessing? Run a fixed eval set several times at a few settings, score correctness and variety, and keep the lowest value that gives the variety the task needs.

What you can say

  1. Temperature doesn't change what the prompt says. It controls how tightly the model sticks to the reading the prompt makes most likely.
  2. Low temperature sharpens the next-token distribution toward the likeliest tokens. High temperature flattens it, so less likely tokens get picked.
  3. That's why the two interact. A specific, well-constrained prompt already narrows the distribution, so it stays on task at a higher temperature, while a vague prompt at 1.0 drifts.
  4. In practice I run extraction, classification, JSON and tool calls at 0 to 0.2, summaries and support replies at 0.3 to 0.7, and brainstorming or varied copy at 0.7 to 1.0.
  5. Temperature 0 is near-deterministic, not guaranteed, and I tune either temperature or top_p, not both.
  6. Some newer models take the choice away. The newest Claude models reject a non-default temperature and Google advises keeping Gemini 3 at its default, so there I steer variety through the prompt.

Weak answers to avoid

  • Says temperature just controls creativity That's a label, not a mechanism. Explain that it sharpens or flattens the next-token distribution, and that the prompt decides what that distribution looks like.
  • Treats temperature 0 as fully deterministic It's near-deterministic, not guaranteed. Say so, and don't build tests or caching that expect identical text on every call.
  • Lowers temperature to fix a vague prompt Turning temperature down on a vague prompt hides the problem. Tighten the prompt first. A well-constrained prompt stays on task even at higher settings.
  • Assumes every model accepts a custom temperature The newest Claude models return a 400 for a non-default temperature, and Google advises keeping Gemini 3 at its default. Say you steer variety through the prompt there.
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