A volunteer-shift scheduler tester pastes the same user story twice and receives two different case lists. What Chapter 1 explanation fits best?
Select an answer to reveal the explanation.
Short Explanation
Same recipe, slightly different cakes—because the oven samples as it goes. Probabilistic inference can produce different outputs from one unchanged prompt. That is non-determinism, not proof the requirement changed.
Full Explanation
LLM generation is probabilistic, so identical prompts can produce different case lists or wording. Chapter 1 asks testers to recognize that non-determinism without treating it as a changed requirement. Detailed temperature or seed mitigations belong later and are not required here. Tokenization and embeddings do not redefine themselves as random deletion or opposite meanings between identical pastes.