Design a Prompt That Asks Clarifying Questions Before Answering

Helps you design a prompt that asks clarifying questions before answering quickly, while still surfacing the open questions a real version of this work would need answered.

Prompt Engineeringadvanced8 minNo ratings yet

When to use this

Use when you need a second opinion on how to design a prompt that asks clarifying questions before answering and want the reasoning shown, not just an answer.

Prompt template

Act as a prompt engineer. Explain the reasoning behind structural choices (why a constraint or instruction is phrased a certain way), not just the final prompt text, so it can be adapted later.

Task: Design a prompt that asks clarifying questions before answering.

Context to use:
- Existing prompt or goal: {{existing_prompt_or_goal}}
- Target model or platform: {{model_or_platform}}
- Constraints: {{constraints}}

Response format:
1. Brief recommendation or approach
2. Main deliverable, organized into clear sections
3. Assumptions and unresolved questions
4. Validation or quality checklist

Customize

Existing prompt or goal is required

Preview

Act as a prompt engineer. Explain the reasoning behind structural choices (why a constraint or instruction is phrased a certain way), not just the final prompt text, so it can be adapted later.

Task: Design a prompt that asks clarifying questions before answering.

Context to use:
- Existing prompt or goal: {{existing_prompt_or_goal}} missing
- Target model or platform: 
- Constraints: 

Response format:
1. Brief recommendation or approach
2. Main deliverable, organized into clear sections
3. Assumptions and unresolved questions
4. Validation or quality checklist

Usage instructions

Replace the placeholders with specifics from your situation, then treat the output as a first draft to edit, not a final answer.

Example output

A structured write-up covering the approach, the concrete steps or artifacts needed, open questions, and a checklist to confirm it's ready to use.

Limitations

The assumptions listed are the model's best guess when context is thin; confirm them against your actual project before proceeding.

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