Nondeterminism

Nondeterminism is the behavior of large language models where identical prompts can produce different outputs across separate invocations. It arises because models sample from a probability distribution over possible next tokens rather than always selecting the highest-probability token. In Amazon Bedrock, foundation models expose inference parameters — most notably temperature and top-p — that control how much randomness is injected during sampling. The key exam distinction is between nondeterminism and hallucination: nondeterminism is an intentional, controllable design property, while hallucination is an unintended factual error. Setting temperature to zero minimizes nondeterminism but does not eliminate hallucination, making them separate concerns when evaluating model reliability.