PlaybookPrompts

Build a branching troubleshooting script from a bug report

Customer Support troubleshootingtechnical-supportscripts

When a recurring bug has multiple causes, reps waste time asking the same scattershot questions. This prompt turns a known issue into a structured decision tree reps can follow without guessing.

Prompt 
You are a support operations specialist. I'm going to describe a technical issue our customers report. Build a branching troubleshooting script that a front-line support rep can follow in a live chat or email thread.

1. Start with the single best first-question to ask the customer that will split the most cases into distinct paths.
2. For each answer branch, provide the next diagnostic step or question.
3. Continue branching until each path reaches one of these outcomes: (a) a self-serve fix the customer can do, (b) a rep-assisted fix with clear steps, or (c) an escalation trigger with the reason.
4. For each escalation trigger, specify what information the rep must collect before handing off.
5. Keep rep instructions in plain language. Mark customer-facing text in [BRACKETS] so it's obvious what to copy-paste vs. what is internal guidance.

Limit depth to 4 levels of branching. If the issue has more than 4 levels of complexity, note which branches need a separate, deeper script.

Issue description: {{ISSUE_DESCRIPTION}}
Known root causes (list what you know): {{KNOWN_ROOT_CAUSES}}
Product area: {{PRODUCT_AREA}}

Note: This script assumes the rep can ask follow-up questions. If your channel is one-shot (e.g., async ticket), adjust step 1 to be a multi-part intake question instead.
Variables to fill in
  • {{ISSUE_DESCRIPTION}}
  • {{KNOWN_ROOT_CAUSES}}
  • {{PRODUCT_AREA}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{ISSUE_DESCRIPTION}}{{KNOWN_ROOT_CAUSES}}{{PRODUCT_AREA}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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