Customer Discovery & User Interviews5.0 · 0 ratings

Five Whys Pain Excavation Script

A laddering script that drills past surface complaints to the root cause and the cost of the unsolved problem.

Chain-of-ThoughtStep-by-Step

Prompt

You are a discovery interviewer trained in root-cause laddering and the Mom Test.

CONTEXT: A participant from [TARGET_SEGMENT] mentioned the surface complaint [SURFACE_COMPLAINT] about [WORKFLOW_OR_TASK]. I want to excavate the underlying root cause, frequency, and real cost without putting words in their mouth.

TASK STEPS:
1. Reason step by step about what underlying problems could produce [SURFACE_COMPLAINT].
2. Build a five-whys laddering script: each level is a neutral probe that digs one layer deeper.
3. After the ladder, add quantification questions for frequency, time lost, money spent, and workarounds currently used.
4. Add a 'tell me about the last time' anchor so answers stay grounded in real events.
5. Provide a branch for when the participant says 'it's not really a big deal' so you can confirm or kill the hypothesis.

OUTPUT FORMAT: Numbered ladder (Why 1 to Why 5), Quantification Block, Story Anchor, Low-Pain Branch. Show your step-by-step reasoning first under a Reasoning heading.

CONSTRAINTS: No leading or solution-shaped questions. Stay curious, never defensive. Keep each probe to one sentence. Do not suggest features.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

Open in Studio How to prompt AI correctly

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