What does the `temperature` parameter actually control, and why would you set it to 0 for some use cases?

Temperature controls how much randomness is injected when the model picks its next token — near 0 makes it almost always pick the highest-probability token (deterministic, consistent output), while higher values increase variety and creativity at the cost of predictability. Structured extraction or classification tasks want temperature near 0 for consistent, repeatable results; creative writing wants it higher.