A municipal softball-diamond desk must decide if Saturday is a rain delay, which games move, and who gets the SMS. Which prompt pattern fits that multi-step job?
Select an answer to reveal the explanation.
Short Explanation
Think of rain delay, which games move, who gets the SMS. Ask the model to work the steps. That is chain-of-thought, not a slogan or an exclusion list.
Full Explanation
Deciding whether Saturday is a rain delay, which games move, and who gets the SMS is a multi-step reasoning job. Chain-of-thought prompting asks the model to work through intermediate steps before the final answer so dependencies stay explicit (weather decision → schedule changes → notification list). For municipal softball desks, that structured reasoning reduces skipped steps and contradictory outcomes in one reply.
A one-word slogan cannot carry intermediate decisions about games and SMS recipients. Zero-shot lookup of a single field with no steps under-specifies a workflow that has branching consequences. A negative prompt that only forbids neon stickers addresses content exclusions, not multi-step operational reasoning.
Exam caveat: chain-of-thought can increase tokens and may expose reasoning you do not want end users to see—use it in staff tools or keep a hidden scratchpad pattern when the public channel should receive only the final notice.
Operational check: feed a scenario with conflicting field conditions, require stepped reasoning, and verify the final SMS list matches only the games actually moved; reject prompts that jump straight to notifications without an explicit delay decision.