Temperature: Trading Determinism for Variation by Step Type
Explore how to adjust temperature settings in AI workflows to achieve reliable outputs for classification tasks and varied text generation. Understand the tradeoffs between output consistency and variation, and learn to set temperature per workflow step to optimize routing stability and natural drafting while managing costs and rework.
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A support workflow often has different consistency requirements across its steps. The triage step should route equivalent inputs consistently to the same queue because inconsistent routing can create backlogs and rework. The draft reply step can allow more variation so responses sound natural rather than copy-pasted across similar cases.
Temperature controls how much an LLM’s output can vary across repeated runs on the same input. At higher temperatures, the model is more likely to produce varied wording and, in some tasks, different classifications or choices. At lower temperatures, the model is more likely to produce consistent outputs across repeated runs.
How temperature behaves in two steps
Using the same customer email for both steps makes the tradeoff visible. In triage, the output is usually structured, like {“category”: “billing”, “priority”: “high”, “next_action”: “escalate”, “needs_human”: false}, and any change in category or priority across reruns is a routing defect. In a draft reply, variation can be acceptable if the meaning stays aligned and the tone stays on policy.
The key difference is what counts as success. ...