Root Cause Tagging — AI workflow for Malaysian SMEs
Root Cause Tagging turns closed tickets into issue taxonomy. AI writes the first draft. You check it before it goes out.
Ready-to-use prompts for Root Cause Tagging
Create prompt
Create prompt
Create a first-pass deliverable
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
The job: root cause tagging. I will give you closed tickets. Turn it into issue taxonomy. Keep it practical for SMEs handling repetitive customer questions through email, chat, phone or tickets. Use short sections, end with one recommended next action, and give me a version I can paste straight into the workflow.
Replace “closed tickets” with your real data.
Analyse prompt
Analyse prompt
Analyse the input and identify the highest-impact insights
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
I need analysis on root cause tagging. You get closed tickets. Find the five findings worth acting on, rank them, and attach the likely business impact to each. Turn the result into issue taxonomy. The last section: three concrete steps to move repeat issue rate.
Replace “closed tickets” with your real data.
Optimise prompt
Optimise prompt
Improve an existing version for better business results
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Take my current root cause tagging version and improve it. I supply closed tickets and the existing results. Find the weaknesses, kill the busywork, tighten the output. Deliver the improved issue taxonomy. List every change with the problem it fixes and the repeat issue rate effect I should expect.
Replace “closed tickets” with your real data.
Automate prompt
Automate prompt
Turn the task into a repeatable AI-assisted workflow
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Turn root cause tagging into an automated workflow. It starts when closed tickets arrives. It must end with issue taxonomy. Available systems: Help desk, email, chat, CRM, knowledge base. Map out: trigger, fields to capture, AI step, decision rules, where a human approves, what happens on failure, data to store. Also repeat issue rate tracking. Keep the whole thing maintainable by a small team.
Replace “closed tickets” and the listed systems with what you actually run.
New here? How the four prompts work together
They are one sequence, not four options. Run them in order on the same piece of work, feeding each answer into the next.
Create
Paste your real data. You get a first draft. Most people never need to go further than this.
Analyse
Paste that draft back in. It tells you what is weak, missing or wrong before a customer sees it.
Optimise
Paste the draft again with what Analyse found. You get a stronger version.
Automate
Only once the output has been good five times running. This is the one people reach for too early.
Nothing forces you through all four. A good Create draft that you edit yourself is a finished job. The longer version.
Specification
- You give it
- Closed tickets
- You get back
- Issue taxonomy
- Moves
- Repeat issue rate
- Run it
- Daily
- Priority
- P0
- Works with
- Help desk, email, chat, CRM, knowledge base
Is this for you?
- Pick it for
- SMEs handling repetitive customer questions through email, chat, phone or tickets. No setup needed. If you can copy and paste, you can run it.
- What you get
- Drafted in minutes, not built from scratch. It aims at productivity. Watch one number: repeat issue rate.
- Reach for it when
- Most SMEs run it daily. If you are doing it by hand more often, automate it first.
How to run it
- 01Collect the input: closed tickets.
- 02Copy the Create prompt and paste it into ChatGPT, Claude or Gemini.
- 03Replace “closed tickets” in the prompt with your real data, then run it.
- 04Read the output against what you already know and correct anything the model guessed at.
- 05Track repeat issue rate. Only wire up the Automate prompt after five useful runs in a row.
The tools you will need
The prompts run in any chat assistant — ChatGPT, Claude or Gemini. These are what you need around them to get the input in and the output somewhere useful.
- chat
- Any tool you already use is fine.
- knowledge base
Check the rules yourself
This workflow touches rules set by someone else. Check the current requirements yourself before you act on the output.
- JPDP
- Pasting customer or staff records into an AI tool is a transfer of personal data. Consent, notice, retention and cross-border rules still apply when the tool is not yours.
Links go to each authority’s own site. See all Malaysian authorities.
Data & privacy
This workflow involves customer names, contact details and support messages. Under the PDPA your business stays responsible for it, and pasting it into an AI tool may be a disclosure to a third party. Replace names and numbers with initials or references first — the prompt works just as well on “Customer A”.
Using customer data with AI tools in Malaysia covers what to check with any provider before you paste.
For this website, it works differently.
Nothing you type here is sent to us or to an AI model. The prompts are static content, and the copy button works directly inside your browser.
Related workflows
- Escalation DetectionTurns ticket text and history into escalation flag and reason.
- First ResponseTurns ticket and customer history into accurate first reply.
- Knowledge Article RecommendationTurns customer question into relevant article suggestion.
- Refund Request HandlingTurns request and policy into policy-aligned response.
All customer service & help desk workflows: Customer Service & Help Desk
Maintained by Naven Pillai