Temperature and Sampling in LLMs: Controlling AI Creativity vs Accuracy
Reviewed: June 4, 2026
Every time you interact with an AI model, there’s a hidden dial controlling whether it plays it safe or gets creative. That dial is temperature — and understanding it is essential for getting the best results from any LLM.
What Is Temperature?
Temperature is a parameter (typically 0.0 to 2.0) that controls the randomness of a model’s output. It works by scaling the model’s logits (raw output scores) before converting them to probabilities.
Low temperature (0.0-0.3): The model becomes deterministic, always picking the highest-probability token. Output is predictable, focused, and repetitive.
Medium temperature (0.5-0.8): Sweet spot for most tasks. The model is coherent but has room for variation.
High temperature (1.0-2.0): The model becomes creative, surprising, and potentially incoherent. Good for brainstorming, bad for facts.
The Math (Simplified)
# Before temperature
probabilities = softmax(logits)
# After temperature
probabilities = softmax(logits / temperature)When temperature → 0, the highest logit dominates (deterministic). When temperature → ∞, all tokens become equally likely (random).
Top-p (Nucleus Sampling)
Temperature isn’t the only knob. Top-p (nucleus sampling) dynamically restricts the vocabulary:
- Top-p = 0.1: Only consider the top 10% most likely tokens
- Top-p = 0.9: Consider enough tokens to cover 90% of probability mass
Best practice: use temperature AND top-p together. Temperature controls the shape, top-p controls the range.
Practical Guidelines
| Task | Temperature | Top-p |
|---|---|---|
| Code generation | 0.0-0.2 | 0.1-0.5 |
| Factual Q&A | 0.1-0.3 | 0.1-0.3 |
| Summarization | 0.3-0.5 | 0.5-0.8 |
| Conversational AI | 0.5-0.8 | 0.7-0.9 |
| Creative writing | 0.8-1.2 | 0.8-0.95 |
| Brainstorming | 1.0-1.5 | 0.9-0.95 |
Common Mistakes
Using temperature for everything: Temperature affects token selection, not factual accuracy. For knowledge tasks, use RAG, not temperature.
Maxing out for creativity: Temperature above 1.5 often produces incoherent output. If you need more variety, try multiple generations with moderate temperature.
Ignoring it: Default temperature (usually 1.0) is mediocre for most tasks. Always set it intentionally.
Bottom Line
Temperature and sampling are the most underutilized settings in AI development. Five minutes of tuning on these parameters can dramatically improve your model’s output quality for any given task.