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

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

**TL;DR.** The model outputs a **probability distribution** per token; sampling params shape the pick. **Temperature** rescales confidence (0 = deterministic-ish, high = diverse), **top-p** keeps the smallest set of tokens whose probabilities sum to p, **top-k** caps candidates. Low for extraction, higher for creative work — and tune **one**, not both. temperature 0.1 peaked → picks the winner temperatur

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

temperaturetop-psamplinginference

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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..." }],
});

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