Temperature, Top-p, and You: Tuning Model Creativity
Two knobs quietly shape almost every answer a model gives you: temperature and top-p. Most people never touch them, which is fine for chat — but if you're generating at scale or wiring prompts into a product, understanding them is the difference between output that's reliably on-target and output that surprises you at the worst moment.
What they actually do
At each step, the model has a ranked list of possible next words with probabilities. These two settings decide how adventurously it picks from that list.
- Temperature flattens or sharpens the probabilities. Low temperature makes the model favor the most likely word (focused, repeatable). High temperature spreads the odds (varied, surprising, sometimes off).
- Top-p (nucleus sampling) caps which words are even eligible — it keeps only the smallest set of top words whose probabilities add up to
p. Low top-p means "only consider the safe, obvious options."
They overlap, so you usually tune one and leave the other near default rather than cranking both.
Sane defaults by task
| Task | Temperature | Why |
|---|---|---|
| Factual answers, extraction | 0.0 – 0.3 | You want the same right answer every time |
| Coding | 0.0 – 0.2 | Determinism and correctness over flair |
| Summaries, rewrites | 0.3 – 0.5 | Faithful but readable |
| Marketing copy, brainstorming | 0.7 – 1.0 | Variety is the point |
If output feels robotic, nudge temperature up. If it drifts, rambles, or invents things, bring it down. Change one setting at a time, or you won't know which knob did what.
The catch most people miss
Higher creativity settings raise the odds of confident nonsense. For anything where accuracy matters — numbers, code, citations — lower is safer, and you make up for the "boring" by writing a richer prompt rather than a hotter setting. A specific, well-structured prompt at temperature 0.2 beats a vague one at 0.9 almost every time.
That's the real lever: the prompt does more for quality than the sampling settings do. If you want to see it, take a flat result, run it through the AI Prompt Refiner to add role, format, and constraints, and compare — usually you'll get the "creativity" you wanted without touching a single knob. And when you're choosing which model to run at all, our guide to picking a model by task pairs naturally with getting these settings right.