Probabilistic

A fundamental characteristic of how generative AI models operate: outputs are generated by sampling from learned probability distributions over possible responses rather than by executing logical rules or retrieving stored facts. Even when configured for deterministic operation, a probabilistic model is selecting the statistically most likely output based on training patterns, not computing a provably correct answer. This distinction explains why models can be confidently wrong (hallucination), why explanations of "reasoning" may be post-hoc rationalizations, and why traditional software warranties and performance guarantees require adaptation for AI systems.