Jobs-to-be-Done (JTBD) Customer Problem Discovery Protocol
Uncover hidden functional, emotional, and social customer jobs, habit inertia, and anxieties preventing switching.
Compatibility & Specs
205 words • 1444 characters
Customize Prompt
Fill in the variables below. Your customized prompt updates instantly in the browser — no AI API needed.
The software, service, or workflow being investigated
The primary practitioner or decision-maker
How users currently solve or tolerate this pain
The specific moment friction becomes urgent or unbearable
How to Use This Prompt
Follow this 3-step workflow to extract high-signal responses from any compatible AI model.
1. Tailor the Parameters
Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.
2. Send to AI Model
Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot. These models follow structured multi-step constraints reliably.
3. Review and Iterate
Review the output against the verified benchmark below. Follow up in the conversation to stress-test edge cases or refine tone.
Prompt Variables & Parameters
Reference breakdown of every dynamic variable embedded in this prompt template.
| Placeholder | Parameter Name | Type | Status | Description & Guidance |
|---|---|---|---|---|
| [product_context] | Product Context / Domain | text | Required | The software, service, or workflow being investigatedDefault: Team documentation and engineering runbook search tool that links Slack, GitHub, and Google Docs. |
| [target_user] | Target User Persona | text | Required | The primary practitioner or decision-makerDefault: On-call software engineers and tech leads at fast-growing scaleups (50-200 engineers). |
| [current_workarounds] | Current Workarounds & Incumbents | textarea | Required | How users currently solve or tolerate this painDefault: Pinging teammates on Slack ('Hey who knows how to restart the staging pipeline?'), searching fragmented Confluence pages that haven't been updated in 9 months, or grep-searching repo history. |
| [trigger_event] | Primary Trigger Event | text | Required | The specific moment friction becomes urgent or unbearableDefault: A high-severity PagerDuty incident occurs during off-hours, and the on-call engineer spends 40 minutes hunting for an updated runbook while latency spirals. |
Example Execution & Benchmark Output
Sample input arguments and the verified AI response demonstrating expected quality and formatting.
Best Use Cases
Scenarios and roles where this prompt produces maximum leverage.
Tips for Best Results
Techniques to elevate response fidelity
- •Explicitly define your Ideal Customer Profile (ICP), their acute pain points, and current legacy alternatives.
- •Provide strict negative constraints: list corporate clichés, overused jargon, or vague promises the model must avoid.
- •Ask the model to critique its own copy from the perspective of a cynical, time-pressed prospect before finalizing.
Common Mistakes to Avoid
Frequent failure modes and anti-patterns
- •Failing to define a singular, clear Call-to-Action (CTA), resulting in unfocused and diluted messaging.
- •Accepting high-level marketing buzzwords that sound impressive but say nothing concrete to users.
- •Not fact-checking competitive claims, legal guarantees, or pricing specifications generated by the model.
Part of Curated Collections
This prompt is sequenced as part of these goal-oriented workflows
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