Why does AI give different answers to the same question?
Updated September 2026
Because these models are probabilistic. They do not look up one fixed answer; they sample the next word from a distribution of likely options, so the same question can produce different answers on different runs. The randomness is a feature of how they generate language, not a malfunction.
Two things drive the variation. First, decoding is a sampling process, so the model draws from a range of likely next words rather than always taking the single most likely one. Second, even with the randomness dial turned to zero, the output is not perfectly repeatable, because of how floating-point math is ordered on the hardware and how large models route a request through their internal experts. Models can be run in a strictly deterministic, greedy mode, but that tends to produce dull, repetitive text, so systems choose sampling for quality.
The practical consequence is the reason the measurement rules exist. If the same prompt can yield different sources on different runs, you cannot judge your AI visibility from one answer. You sample many runs and report the range, which is exactly how a real visibility measurement is built.