Reverse Interview Diagnostic
Key Capabilities
- Pre-emptive Context Gathering: Forces the AI to identify missing information instead of hallucinating assumptions.
- Blind Spot Detection: Automatically points out critical angles or edge cases you might not have considered in your workflow.
- Tailored Problem Solving: Defers the actual solution generation until a complete contextual picture is formed through Q&A.
Underlying Mechanism
This prompt utilizes a "Reverse Interview" or "Diagnostic" constraint. Instead of allowing the LLM to output a direct (and often shallow) solution based on a single prompt, it establishes an initial state of inquiry. By explicitly mandating the AI to ask exactly 5 clarifying questions, you shift the cognitive load of "knowing what to prompt" onto the model itself.
Ideal Use Cases
- Starting a complex new project where the exact requirements are still fuzzy.
- Making difficult strategic decisions where you want an objective diagnostic lens.
- Overcoming a specific blocker when you aren't sure how to articulate the root cause.
When NOT to Use (Anti-Patterns)
- Routine, deterministic tasks where you already know exactly what the output should look like (e.g., formatting data, simple code generation).
- Situations where you need an immediate output without interactive back-and-forth.
Pro Tip
Once the AI provides the 5 questions, you don't have to answer them perfectly. You can answer the ones you know and tell the AI to "make reasonable assumptions" for the rest to proceed to the solution phase.
Source Reference: Curated Best Practices for Reverse Interview Prompting
How to Use This Prompt
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Fill in Variables
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Run in AI Model
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