Large language models generate text by sampling words from probability distributions, where each word has a selection probability; the temperature parameter controls this randomness—low temperature sharpens the distribution so the highest-probability word is selected almost always, while high temperature flattens it so any plausible word has a chance, which is why the same prompt can produce different answers across multiple calls.
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Why the same prompt gives different answers — temperature, explained in 40 secondsAdded:
You send the same prompt twice. You get two different answers. That's on purpose. Every time the model picks a word, it's choosing from a list. Each word has a probability. The highest one is the safe bet. There's a knob called temperature. Low temperature sharpens the list. The top word wins almost every time. High temperature flattens it. Now any word has a shot. So the exact same prompt rolls. Different dice every call.
Same question, different answer every time.
Need reproducibility? Set temperature to zero and pass a seed. You'll still see tiny drift from GPU scheduling, but the big randomness goes away.
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