Inside temperature and sampling: how AI chooses the next word
aiexplained
Temperature, top-k, and top-p decide whether your AI sounds creative or boring. A plain-English deep dive into the dials that shape every answer.
When a language model picks its next word, it doesn't just grab the top choice. It samples from a ranked list, and three dials control how adventurous that pick is: temperature, top-k, and top-p. Understanding them is the difference between a model that invents facts and one that stays on script.
Temperature: the creativity knob
Temperature rescales the probabilities before sampling. At 0, the model always picks the single most likely token — deterministic, repeatable, often repetitive. At 1.0, it samples from the full distribution as the model saw it. Above 1.0, low-probability words get amplified, which is where "creative" turns into "weird."
For factual answers — compliance text, financial summaries, medical notes — use 0 or 0.2. For brainstorming or ad copy, 0.7 to 0.9 is a reasonable range.
Top-k: hard cutoff on the shortlist
Top-k limits sampling to the k most probable tokens. Top-k = 1 is greedy decoding. Top-k = 40 means the model will never pick a word outside the top 40 candidates, which blocks genuinely absurd choices without over-constraining phrasing.
Top-p (nucleus sampling): dynamic shortlist
Top-p keeps the smallest set of tokens whose probabilities sum to p. At p = 0.9, the model considers however many tokens it takes to cover 90% of the probability mass. When the model is confident, that's 2 or 3 words; when it's uncertain, it might be 50. Top-p usually produces more natural variation than a fixed top-k.
How these interact
Most APIs apply top-k and top-p first to prune the candidates, then temperature rescales what's left, then one token is sampled. Set temperature to 0 and the other dials become irrelevant — the model picks the single top token every time.
What this means for enterprise deployments
Two users on the "same" model can get very different answers if these settings differ. Lock them per use case: deterministic for anything auditable, modest randomness for drafting, higher only for ideation. That discipline is part of our Responsible AI-by-Design Framework.