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mediumAI Engineering

How do temperature, top-p and other sampling parameters affect output?

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01

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The dials that trade determinism for creativity — and when to turn each one.

temperaturetop-psamplinginference
02

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03

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The model outputs a probability distribution over the next token; sampling parameters shape how you pick from it. Temperature rescales the distribution (0 = near-greedy/deterministic, higher = more diverse), top-p keeps only the smallest set of tokens whose cumulative probability reaches p, and top-k caps the candidate

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04

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Different settings per task
// extraction: deterministic and schema-bound
await client.messages.create({
  model, temperature: 0, max_tokens: 500,
  messages: [{ role: "user", content: extractPrompt }],
});

// brainstorming: allow diversity
await client.messages.create({
  model, temperature: 1.0, max_tokens: 800,
  messages: [{ role: "user", content: "Give 10 campaign angles for..." }],
});
05

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