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Setting AI temperature to zero does not make its answers true

Temperature changes how a model selects tokens. It can reduce variation, but it does not check sources, and some models work best with their default settings.

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Temperature is a sampling control, not an accuracy switch. Turning it down can reduce variation in an AI model's wording without checking a date, finding a missing source or verifying a quotation. A model can become more consistent while remaining consistently wrong.

If your chatbot has no temperature slider, you are not necessarily overlooking one. The control is often exposed through developer tools or local software. Supported values and recommended settings depend on the particular model.

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Temperature changes the next-token choice

Models produce text in pieces called tokens, assigning probabilities to possible continuations. Hugging Face's generation reference describes temperature as a way to change that distribution. Lower values favor already likely continuations. Higher values can leave more room for alternatives.

Likelihood is not a verdict about facts. When a model has the wrong association between a book and an author, narrowing its choices can preserve that mistake. Our guide to checking AI citations and DOIs focuses on finding the actual source.

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02

Top p is a different control

Top p limits candidate tokens using their cumulative probability. Google's prompting guide explains the distinction. Neither setting supplies missing information. Adjusting both together also makes it harder to determine which change helped.

Do not assume minimum values are best. Google recommends defaults for temperature, top p and top k on Gemini 3.x, warning that changes can cause loops or degraded performance. Some local generation strategies do not use sampling at all.

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03

Test correctness separately from consistency

Begin with the recommended configuration. Keep the model, prompt and source document unchanged, then ask for a task you can verify, such as extracting amounts from a table. Compare every amount with the document and repeat before changing a single parameter.

Track factual mistakes separately from wording changes. Identical outputs are not proof of accuracy, and different wording can preserve the same correct answer. If evidence is missing, provide it directly or use retrieval, as described in our RAG explainer. Lower temperature cannot supply that evidence.

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