LLM temperature & parameter guide
Sampling parameters quietly decide whether your LLM feels reliable or unhinged. Set temperature too high on a coding task and you get plausible-looking nonsense; set it too low on a brainstorm and you get the same three ideas every time. This guide maps the common task types to sensible starting parameters and explains the reasoning so you can adjust with intent instead of guessing.
What temperature actually does to the output distribution
Temperature rescales the raw logit scores the model produces before the softmax that converts them into probabilities. At temperature 1.0, the distribution is unchanged from what the model learned — the most likely token might be 30× more probable than a rare one. At temperature 0.1, that gap is amplified enormously; the top token becomes near-certain. At temperature 1.5, the gap is compressed; unlikely tokens get a meaningful share of the probability mass.
The practical effect:
- Low temperature (0–0.3): Predictable, consistent, closely follows the most likely continuation. Ideal when correctness matters more than variety. Results converge quickly across multiple runs.
- Medium temperature (0.4–0.7): Balanced. Good for Q&A, summaries, and explanations where you want the model to follow the most likely path while occasionally using a more natural word choice.
- High temperature (0.8–1.2): Creative and varied. Different runs produce meaningfully different outputs. Risk of incoherence rises as you approach and exceed 1.0 on complex tasks.
- Above 1.2: Useful only for narrow creative tasks with short outputs; coherence breaks down on longer generations for most models.
How it works
Pick the task type and how deterministic you need the output to be. The tool returns a recommended temperature, top-p, and max-tokens, plus a short rationale. The logic follows the well-established trade-off: deterministic, correctness-critical tasks (code, extraction, classification) sit near temperature 0, balanced tasks (Q&A, summarisation) sit in the 0.2–0.5 band, and generative, divergent tasks (storytelling, brainstorming, marketing copy) climb toward 0.8–1.2. The determinism slider nudges the recommendation within the band so you can lean more consistent or more varied.
Quick reference by task type
| Task | Suggested temperature | Rationale |
|---|---|---|
| Code generation | 0 – 0.1 | Correctness is binary; variety is unwanted |
| Data extraction / classification | 0 – 0.2 | Schema compliance matters; creativity does not |
| Factual Q&A | 0.2 – 0.4 | Accurate answers with natural phrasing |
| Summarization | 0.3 – 0.5 | Balanced fidelity and readability |
| Writing assistance / editing | 0.5 – 0.7 | Helpful suggestions without losing voice |
| Brainstorming / ideation | 0.8 – 1.0 | Variety is the point |
| Creative writing | 0.9 – 1.2 | Distinctive, surprising outputs |
Tips and notes
Change temperature or top-p, not both — combining them makes results hard to
reason about. For anything you will run repeatedly (tests, pipelines, eval suites)
pin temperature to 0 so results are reproducible. If outputs feel repetitive at
moderate temperature, a small bump plus a frequency_penalty often helps more
than a large temperature jump. And remember that the “right” setting is the one
that passes your own evaluation on real inputs — treat these as informed starting
points, then measure.