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Non-determinism is the property of large language models that causes the same prompt to produce different responses when run multiple times. This is not a bug — it is a fundamental characteristic of how LLMs generate text by sampling from probability distributions at each token.

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Why it matters

Non-determinism makes single-run AI visibility measurements unreliable. A brand mentioned in one ChatGPT response might not appear in the next response to the identical prompt. Robust measurement requires multiple runs per query and statistical aggregation — a single snapshot tells you what happened once, not what typically happens.

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How Cited uses it

Cited accounts for non-determinism by running prompts multiple times per platform and aggregating before reporting. Dashboard metrics are based on aggregated data, not single runs. See Non-determinism for practical guidance on interpreting metric movements.