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BusinessPRDsintermediate

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

Compatible AI Models
ClaudeChatGPTGemini
Last UpdatedOct 3, 2026
Customizable Variables4 parameters

How to Use This Prompt

Follow this 3-step workflow to extract high-signal responses from any compatible AI model.

01

1. Tailor the Parameters

Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.

02

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.

03

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.

PlaceholderParameter NameTypeStatusDescription & Guidance
[product_context]Product Context / DomaintextRequiredThe software, service, or workflow being investigatedDefault: Team documentation and engineering runbook search tool that links Slack, GitHub, and Google Docs.
[target_user]Target User PersonatextRequiredThe primary practitioner or decision-makerDefault: On-call software engineers and tech leads at fast-growing scaleups (50-200 engineers).
[current_workarounds]Current Workarounds & IncumbentstextareaRequiredHow 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 EventtextRequiredThe 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.

Sample VariablesInputs
[product_context]
Team documentation and engineering runbook search tool that links Slack, GitHub, and Google Docs.
[target_user]
On-call software engineers and tech leads at fast-growing scaleups (50-200 engineers).
[current_workarounds]
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]
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.
Expected AI ResponseVerified Result
### 1. The Core Jobs-to-be-Done Hierarchy - **Functional Job:** Resolve production incidents accurately under time pressure without relying on synchronous tribal knowledge. - **Emotional Job:** Feel confident, calm, and self-sufficient when waking up to an alarm at 3:00 AM. - **Social Job:** Be viewed by leadership and peers as a reliable, competent on-call engineer who resolves incidents swiftly without causing customer escalations. ### 2. The Four Forces of Progress - **Push of the Present:** The humiliation and stress of escalating to the VP of Engineering at 3 AM because the deployment documentation is broken and out-of-date. - **Pull of the New Solution:** The promise of a single search command in Slack that surfaces the verified, tested terminal command within 5 seconds. - **Anxiety of the New:** Fear that installing another tool will just create a *third* out-of-date documentation silo that requires manual maintenance. - **Habit Inertia:** The subconscious reflex to just type '@here does anyone know where the Redis cluster keys are?' in Slack because it requires zero cognitive effort. ### 3. High-Leverage JTBD Discovery Questions 1. *'Think back to the last time an incident went over SLA. At what exact minute did you realize you didn't have the info you needed, and what was the very next tab you opened?'* 2. *'When you realized the doc was outdated, who did you message, and how long did you wait before guessing the command yourself?'*

Best Use Cases

Scenarios and roles where this prompt produces maximum leverage.

Product managers conducting customer discovery interviews for roadmap prioritization
Founders positioning new tools against entrenched habits and spreadsheets
User researchers synthesizing qualitative interview recordings into executive frameworks

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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