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Decide between chain-of-thought and direct-answer prompting for a specific task

auto-checked, 3 hours old
AauraNovice

The prompt

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Help me decide whether to use explicit chain-of-thought prompting or a direct-answer prompt for this task: task_description, rather than defaulting to 'CoT is always better'. Given the task and 2-3 example inputs: 1. Characterize the task type: is it primarily a multi-step reasoning task (where CoT genuinely helps by giving the model room to work through steps) or a retrieval/classification/formatting task (where CoT often just adds latency/cost without improving accuracy, or can even hurt by giving the model room to talk itself into an over-thought wrong answer). 2. If CoT seems warranted, specify what kind: should reasoning be shown to the user (useful for auditability) or hidden/scratchpad-only (useful when you only want the final answer and reasoning would clutter the response)? 3. If direct-answer seems sufficient, say so plainly and don't hedge into recommending CoT 'just in case', that defeats the point of the comparison. 4. Propose a quick test: 3 inputs where CoT and direct-answer would most likely diverge if there's a real difference, so I can verify the recommendation empirically rather than trusting the theoretical argument alone. Task: task_description Example inputs: example_inputs

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ModelVersionPeopleBrokeAutoLast
ClaudeHaiku 4.5001/13 hours ago

Example output

Task: classify a support ticket into one of 8 fixed categories. This is fundamentally a classification task, not multi-step reasoning, direct-answer is likely sufficient and CoT risks the model second-guessing an initially-correct classification into an incorrect one by over-deliberating on edge-case tickets. Recommendation: direct-answer, not hedged. Skip CoT here; the categories are well-defined enough that the model doesn't need visible reasoning to disambiguate correctly in the large majority of cases. Exception noted: for the subset of tickets that genuinely straddle two categories (roughly the ones your examples labeled 'ambiguous'), a lightweight hidden-scratchpad approach ('think step by step internally, then output only the category') might help specifically there, without cluttering the response with visible reasoning for the 90% of tickets that are unambiguous. Test to verify: run direct-answer and hidden-scratchpad on your 3 hardest example tickets (the ones you flagged as previously misclassified) and compare accuracy on just those; if hidden-scratchpad doesn't measurably improve accuracy on the hard cases either, direct-answer wins outright on cost and latency with no quality tradeoff.

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