Prompt Engineering Prompts
Chain-of-Thought Prompt Converter
Converts a direct-answer prompt into a structured chain-of-thought version that guides the model through explicit reasoning steps, improving accuracy on complex tasks.
FreeClaudeChatGPT
Best for
Developers and prompt engineers working on tasks involving multi-step reasoning, classification with nuanced criteria, or calculations, where direct-answer prompts produce inconsistent or shallow results.
Suitable LLM groups
FrontierReasoning
Prompt
You are a prompt engineering expert who specializes in converting direct-answer prompts into structured reasoning prompts for tasks that benefit from step-by-step thinking.
Original direct prompt: {{original_direct_prompt}}
Task type (e.g., analysis, calculation, classification, decision-making): {{task_type}}
What's currently going wrong or could be more reliable: {{current_reliability_issue}}
Do the following:
1. Identify the implicit reasoning steps a competent human would go through to complete this task correctly (e.g., for a classification task: identify criteria, check the input against each criterion, weigh conflicting signals, then decide).
2. Rewrite the prompt to explicitly instruct the model to work through those steps before giving a final answer, using clear step labels or numbered reasoning stages.
3. Specify exactly how the final answer should be presented after the reasoning (e.g., a clearly marked 'Final answer:' line), so the reasoning process doesn't get confused with the deliverable when the output is used downstream.
4. Explain in 2-3 sentences why this specific task benefits from explicit reasoning steps rather than a direct answer, or if it's actually a task where chain-of-thought adds unnecessary overhead without improving accuracy (be honest about this).How to use
- Paste your current direct-answer prompt along with the task type and any reliability issues you've observed.
- Use the identified reasoning steps to understand what the model actually needs to consider for this task.
- Replace your original prompt with the rewritten chain-of-thought version.
- If the honest assessment says chain-of-thought isn't needed for this task, keep your original direct prompt instead.
Example input
Original prompt: 'Is this customer review positive or negative?' Task type: Classification. Current issue: Struggles with mixed reviews that have both positive and negative elements, defaulting to whichever sentiment appears first.
Example output
Identified reasoning steps: identify all sentiment-bearing statements, categorize each as positive/negative/neutral, weigh the overall balance and the reviewer's apparent final judgment, then classify. A rewritten prompt instructing the model to list sentiment statements first, categorize each, then reason about overall balance before stating 'Final classification:' on its own line, with an explanation that this task benefits from explicit reasoning specifically because mixed-sentiment reviews require weighing conflicting signals rather than pattern-matching to the first sentiment mentioned.
Customization tips
- — For tasks feeding into automated systems, specify the exact format needed for the final answer line so it's easy to parse out from the reasoning text.
- — If accuracy is critical, ask for an additional self-check step at the end where the model reviews its own reasoning for consistency before finalizing.
- — For very simple tasks, explicitly ask whether chain-of-thought is worth the added token cost and latency.
Tags
#chain-of-thought#prompt-engineering#reasoning-prompts#prompt-optimization#ai-accuracy
FAQ
What is this prompt for?
It converts a direct-answer prompt into a structured chain-of-thought version that guides the model through explicit reasoning steps before answering, which improves reliability on tasks with nuanced or conflicting signals.
How should I customize it?
Describe the specific reliability issue you're seeing with the direct prompt, since the reasoning steps generated should directly address the actual failure pattern rather than being generic.
Are there any limitations?
Chain-of-thought prompting adds token usage and latency; it's not worth using for genuinely simple tasks, and the prompt will honestly flag when that's the case rather than always recommending it.
How is it different from a basic prompt?
Rather than just adding 'think step by step' generically, it identifies the specific reasoning steps relevant to the actual task, structures them explicitly, and is honest about when chain-of-thought isn't actually needed for a given task type.
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