Do I need to test multiple temperatures when evaluating prompts?

Usually, yes — if you care about robustness rather than just one sample.

Why test multiple temperatures?

Temperature changes how random the model is:

• Low temperature (e.g. 0–0.3): more deterministic, conservative
• Medium/high temperature (e.g. 0.7+): more varied, creative, but less stable

So a prompt that looks great at one temperature may behave poorly at another.

When it matters

Test multiple temperatures if your prompt is used for:

• creative generation — summaries, brainstorming, writing
• customer-facing output — you want consistency and safety
• tool/function calling — you want reliability
• benchmarking prompt quality — to avoid overfitting to one setting

When one temperature may be enough

If your production setup is fixed and you’ll always use, say, temperature=0, then evaluate at that setting first.
For a narrow, deterministic task, testing only the target temperature is often sufficient.

Practical approach

A good evaluation pattern is:

1. Pick your production temperature
2. Evaluate there first
3. Also spot-check a few others:
   • 0 for determinism
   • 0.3–0.7 for stability
   • higher values if creativity matters

Rule of thumb

• Need reliability? Focus on lower temperatures
• Need creativity? Compare across several temperatures
• Unsure? Test at least 0, 0.5, and your intended production setting

If you want, I can also suggest a simple evaluation matrix for prompts across temperatures.