How One Setting Can Make or Break Your AI Output

Why do engineers get inconsistent results from ChatGPT?

You feed in the same prompt. You expect the same answer. But what you get? A response that feels… off.

That’s not just randomness. It’s temperature — one of the most overlooked settings in generative AI.

What is Temperature in AI?

Temperature controls how predictable or creative the model is.

• A high temperature (like 0.8) makes the AI more exploratory. You’ll get more variation in word choice, tone, and even logic.

• A low temperature (like 0.2) locks it down. The output becomes deterministic — the same inputs give the same outputs, with strict step-by-step logic.

For engineers, this isn’t just theory — it changes how reliable your results are.

What We Found When We Tested It

We ran a simple pump power calculation prompt at three temperature settings: 0.8, 0.5, and 0.2. The prompt stayed exactly the same.

• At 0.8, the response was casual and creative. It answered the question, but in a vague and conversational way — not something you’d paste into a calc sheet.

• At 0.5, the model delivered a good balance. The result was structured and readable, though it still simplified some parts.

• At 0.2, the AI snapped into calculation mode. It returned full formulas, clear units, and consistent logic — perfect for technical workflows.

The difference wasn’t in what was being asked. It was in how the model was allowed to respond.

Why This Matters in Engineering Workflows

If you're using AI to support calculations, documentation, or any spec-driven task, consistency and traceability are critical.

This is why at Singularity, we always test prompts at different temperature settings before finalizing them. We select the output style that best matches the deliverable — whether it’s a precise calc sheet or a readable client summary.

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