Temperature Controls Variability, Not Truth
Temperature changes how varied or deterministic model outputs tend to be.
<ul>
<li>lower temperature usually means more consistent outputs</li>
<li>higher temperature usually means more diverse outputs</li>
</ul>
It does not guarantee factuality or safety by itself.

Match Variability to the Job
Use lower-variance settings for tasks like:
<ul>
<li>extraction</li>
<li>structured classification</li>
<li>grounded support responses</li>
<li>compliance-oriented summaries</li>
</ul>
Use higher-variance settings more cautiously for divergent tasks like ideation or copy exploration.

Which task usually benefits from a lower-temperature setting?

Lowering temperature by itself guarantees factual accuracy.

Tune on Real Product Tasks
The right setting depends on the workflow, user expectations, and review model.
A PM should test temperature on representative tasks and compare:
<ul>
<li>quality</li>
<li>consistency</li>
<li>edit rate</li>
<li>user satisfaction</li>
<li>failure patterns</li>
</ul>

Which task may benefit from a higher-temperature setting?

Creative settings can be useful, but outputs still need review and guardrails in production.

When Temperature Is Not the Problem
If the assistant is wrong because it lacks private or current information, changing temperature will not fix the root cause.
In those cases, the stronger move may be:
<ul>
<li>better retrieval</li>
<li>tool access</li>
<li>clearer instructions</li>
<li>tighter evaluation</li>
</ul>

Users say the assistant invents policy details. What should the PM investigate first?

PMs should evaluate temperature changes using task-specific success metrics, not intuition alone.

Takeaway
Temperature is one tuning lever among many. It matters, but prompt quality, context, tool access, evaluation design, and UX usually matter more to product success.