Customer service teams use AI to draft replies to difficult customers, build quick-reply templates, turn tickets into help-centre articles, classify ticket backlogs and find root causes. The prompts here keep promises realistic and tone human, because a fast but wrong answer creates a second ticket.
The best support replies answer the question in the first sentence, sound like a person and never promise what the company cannot deliver. AI can produce that quickly when it has your policies in front of it - so each prompt asks for them.
What AI helps customer service teams with
- Replying to angry customers calmly
- Building chat and WhatsApp quick replies
- Turning tickets into help articles
- Classifying and routing ticket backlogs
- Writing outage and incident notices
- Finding root causes from conversations
12 AI prompts for customer service teams
Press "Fill in" to complete the [BRACKETS] in a form, then copy or open the prompt straight in ChatGPT, Claude or Gemini. Save the ones you use with the heart.
Respond to an angry customer without escalating
Draft a reply that de-escalates and resolves.
Draft a customer support reply. CUSTOMER MESSAGE: """ [PASTE] """ WHAT ACTUALLY HAPPENED (our side): [THE FACTS, INCLUDING IF WE WERE AT FAULT] WHAT I CAN OFFER: [REFUND / REPLACEMENT / CREDIT / FIX / NOTHING] WHAT I CANNOT DO: [CONSTRAINTS] CHANNEL: [email / chat / public review reply] Write the reply so that it: - Opens by naming their specific problem in their own words, not with an apology template - States plainly whether we got it wrong. If we did, say so without hedging - no 'we are sorry you feel' - Explains what happened only if it helps them, not to defend us. One sentence maximum. - Gives the resolution and the timeline as a concrete date, not 'as soon as possible' - States clearly what we cannot do, once, without repeating it - Ends with the single next step and who takes it Rules: - Under 150 words for chat, under 200 for email - No 'we value your feedback', 'we apologise for any inconvenience', 'rest assured', 'kindly' - Do not ask them to repeat information they already gave - If we were not at fault, do not apologise for the outcome - acknowledge the frustration and move to the fix Also give me: the one sentence in my draft most likely to make them angrier, and why.
What you get: A short, direct reply plus a flag on the riskiest sentence in it.
Tip: Banning 'we apologise for any inconvenience' forces a real apology or none. Customers can tell the difference instantly.
Reply to customer messages on WhatsApp and chat
Handle common chat questions quickly with friendly, clear replies and quick-reply templates.
Help me reply to customer chat messages (WhatsApp Business, Instagram DM, website chat). MY BUSINESS: [WHAT YOU SELL, WHERE YOU DELIVER] KEY INFORMATION: [PRICES, DELIVERY TIMES, PAYMENT METHODS, RETURN POLICY, OPENING HOURS] MESSAGES TO ANSWER: [PASTE] LANGUAGE(S) MY CUSTOMERS USE: [e.g. English, Urdu, Roman Urdu, Arabic] For each message: a reply under 60 words, in the customer's language and style (formal or casual), that answers the question first. Then create QUICK REPLIES for the 10 most common questions (price, availability, delivery, payment, returns, order status, location, hours, bulk orders, complaints), each with a short shortcut name. Rules: never promise delivery dates or stock I have not confirmed; for complaints, apologise once, state the fix, and ask for the order number.
What you get: Short replies in the customer's language, plus ten named quick-reply templates for common questions.
Tip: Replying in the language the customer used - including Roman Urdu or mixed language - builds trust quickly. Most chat tools let you save these as shortcuts.
Write canned responses that do not sound canned
Build a reusable macro library for your most common replies.
Write a set of canned support responses. COMMON SITUATIONS: [LIST 5-10 SITUATIONS] PRODUCT: [WHAT IT IS] BRAND VOICE: [e.g. plain and direct / warm / technical] CHANNEL: [email / chat] For each situation produce: - A macro name (short, searchable) - The response, with [VARIABLES] for anything agent-specific - WHEN TO USE IT - one line - WHEN NOT TO USE IT - one line. This is the important field. - The manual edit an agent must make before sending (there must always be at least one) Rules: - Maximum 120 words each - Every macro must have at least one [VARIABLE] that forces the agent to engage with the specific case - No macro may open with an apology - No 'I completely understand how frustrating this must be' - Include a 'we cannot do that' macro and a 'this is going to take longer than we said' macro - these are the two hardest and most-needed Also flag: any situation in my list where a canned response is the wrong tool and it needs a human from scratch.
What you get: A macro library with mandatory edit points, 'when not to use' guidance, and flagged situations that should never be templated.
Tip: Forcing a mandatory manual edit into every macro is what stops your support inbox sounding like a robot. It costs the agent ten seconds.
Turn support tickets into a help-centre article
Write documentation from the questions people actually ask.
Write a help-centre article from these support tickets. TICKETS (all about the same issue): """ [PASTE 5-15 TICKETS] """ PRODUCT/FEATURE: [NAME] WHO THESE USERS ARE: [CONTEXT] Produce: 1. TITLE - phrased the way users describe the problem, not the way we describe the feature. Pull the phrasing from the tickets. 2. ONE-LINE ANSWER - the fix, immediately, before any explanation. 3. STEPS - numbered, one action each, with the exact UI label in bold. 4. IF THAT DID NOT WORK - the second and third most common causes from these tickets, each with its own fix. 5. WHY THIS HAPPENS - two sentences maximum, and only if knowing it prevents recurrence. 6. RELATED QUESTIONS - the adjacent questions these same users asked in the tickets. Also output separately: - SEARCH TERMS: the exact phrases users typed in these tickets, as a list. These go in the article's metadata. - PRODUCT PROBLEM: if these tickets suggest the product is confusing rather than the user, say so plainly and describe the fix. Documentation should not paper over a bad interface.
What you get: A help article written in the user's language, plus their literal search terms and an honest note on whether the product is the real problem.
Tip: The extracted search terms are the most valuable output. They are free keyword research in your customers' exact words.
Classify and route a backlog of tickets
Impose structure on a pile of unsorted support requests.
Classify these support tickets. TICKETS: """ [PASTE TICKETS, ONE PER LINE OR SEPARATED BY ---] """ OUR TEAMS: [LIST TEAMS AND WHAT EACH HANDLES] For each ticket output a row: ID | Category | Sub-issue | Urgency (P1-P4) | Route to | Sentiment | Churn risk (Y/N/?) | One-line summary. Define urgency as: P1 - customer cannot use the product at all, or data/money at risk P2 - major feature broken, workaround exists P3 - minor issue or confusion P4 - feature request or feedback After the table: - Group the tickets into themes and give a count per theme, sorted by volume - Name the top three themes and, for each, say whether the fix is documentation, product, or process - Flag any ticket where the customer has written in more than once about the same thing - Flag any ticket that is actually a bug report dressed as a question Do not mark something P1 because the customer used urgent language. Use the definitions.
What you get: A routed, prioritised ticket table plus volume themes and a fix-type recommendation per theme.
Tip: The last line matters. Without it, models read tone as priority and everything written in capitals becomes a P1.
Write a service outage or incident notice
Communicate a failure to customers clearly and without spin.
Write a customer-facing incident notice. WHAT BROKE: [PLAIN DESCRIPTION] WHO IS AFFECTED: [SCOPE] WHAT STILL WORKS: [SCOPE] WHEN IT STARTED: [TIME] CURRENT STATUS: [investigating / identified / fix in progress / resolved] WHEN WE EXPECT RESOLUTION: [TIME, or 'unknown'] DATA IMPACT: [none / delayed / lost / unknown] WORKAROUND: [IF ANY] Write three versions: 1. STATUS PAGE - under 100 words. Lead with what is broken and who it affects. Include what still works. State the next update time explicitly. 2. EMAIL TO AFFECTED CUSTOMERS - under 200 words. Same facts. Add: what they should do now, and whether they need to take any action to recover. 3. HOLDING REPLY for support agents to send to individual tickets - under 60 words. Rules: - Never say 'some users' if you know the scope. State it. - If resolution time is unknown, say 'we do not yet have an estimate' and give a next-update time instead. Never guess. - No 'we are aware of an issue affecting some customers and are working to resolve it as quickly as possible' - that sentence says nothing. - If data was lost, say so in the first sentence. - Do not apologise more than once per message.
What you get: Three length-matched versions of the same honest facts for status page, email and ticket replies.
Tip: Always committing to a next-update time, even when you cannot commit to a fix time, is what keeps customers from writing in every ten minutes.
Analyse support conversations for root causes
Find out what is actually generating your ticket volume.
Analyse this batch of support conversations for root causes. CONVERSATIONS: """ [PASTE] """ PERIOD COVERED: [DATES] TOTAL TICKET VOLUME IN PERIOD: [NUMBER, if known] Produce: 1. ROOT CAUSE TABLE - Cause | Ticket count | % of sample | Fix type (product / documentation / onboarding / process / expectation-setting) | Estimated effort (S/M/L) 2. THE TOP THREE causes by volume, each with: a representative customer quote, why it happens, and the specific fix. 3. AVOIDABLE VOLUME - what percentage of this sample would disappear entirely if the top three were fixed. Show the arithmetic. 4. EXPECTATION FAILURES - tickets where the product worked as designed but the customer expected something else. These need marketing or onboarding fixes, not engineering. List them separately. 5. THE SILENT MAJORITY WARNING - for each top cause, note that the customers who wrote in are a fraction of those affected, and say what you would need to measure to size the real impact. Do not extrapolate from this sample to the whole customer base unless I gave you total volume. If I did, state the assumption you are making.
What you get: A root-cause table with fix types, an avoidable-volume calculation, and expectation failures separated from real bugs.
Tip: Section 4 usually contains the cheapest wins on the list. Expectation failures are fixed with a sentence on a pricing page, not a sprint.
Draft a reply to a bad public review
Respond in public without making it worse.
Draft a reply to a negative public review. REVIEW: """ [PASTE REVIEW] """ RATING: [STARS] PLATFORM: [Google / Trustpilot / App Store / other] WHAT ACTUALLY HAPPENED: [OUR SIDE OF IT, HONESTLY] IS THE REVIEW ACCURATE: [yes / partly / no / cannot verify] WHAT WE CAN OFFER: [IF ANYTHING] Remember the audience is not the reviewer. It is the next hundred people reading reviews before they buy. Write a reply that: - Is under 100 words - Addresses the specific complaint, not reviews in general - Corrects a factual error only if it is material, once, neutrally, without arguing - Takes responsibility where we were wrong, plainly - Moves the resolution off the platform with a specific route (named contact or direct address), not 'please contact our support team' - Never asks them to change or remove the review Then give me: - WHAT NOT TO SAY: the two things I will be tempted to write that would make this worse - ESCALATE OR LET IT GO: whether this is worth further effort, and why - If the review appears to be fake or from a non-customer, say so and describe the platform's reporting route instead of replying.
What you get: A short public reply written for future readers, plus a list of the tempting mistakes and a judgement on whether to pursue it.
Tip: Reminding the model that the audience is future buyers, not the reviewer, changes the whole register. Defensive replies cost more sales than bad reviews do.
Reply to comments and DMs quickly and in your voice
Draft replies to common comments and messages, including tricky ones.
Help me reply to Instagram comments and DMs. MY ACCOUNT AND TONE: [DESCRIBE] COMMENTS / MESSAGES TO ANSWER: [PASTE] THINGS I NEVER SAY OR PROMISE: [e.g. prices in comments, medical advice, delivery dates] For each comment or message: - Type: question / compliment / complaint / spam / troll / sales lead - Suggested reply (short, in my tone) - Whether to reply publicly, move to DM, or ignore/hide Also create 6 reusable reply templates for my most common questions, with a [PERSONAL DETAIL] slot so they do not read as copy-paste. Rules: never argue publicly; never share private details; keep complaints calm and move them to DM.
What you get: Categorised replies with a public/DM/ignore decision and six reusable templates.
Tip: The [PERSONAL DETAIL] slot is what stops templates feeling robotic. Mention something specific from their comment.
Respond to a bad review or angry comment publicly
Write a calm public reply that protects your reputation.
Help me respond to a negative review or comment on Facebook. THE REVIEW / COMMENT: [PASTE] WHAT ACTUALLY HAPPENED (my side): [FACTS] WHAT I CAN OFFER: [refund / redo / call / nothing] TONE: [warm / professional] Write: 1. A PUBLIC REPLY - under 80 words: thank them, acknowledge the specific issue, state one fact if needed without arguing, offer to continue privately 2. A PRIVATE MESSAGE to send them 3. What NOT to say (things that would make it worse) 4. Whether this review might break platform rules (e.g. fake, not a customer, hate speech) and how to report it if so Remember: the public reply is written for future customers reading it, not for the reviewer.
What you get: A short public reply, a private follow-up, phrases to avoid and a policy check.
Tip: Future customers judge you by the reply more than the review. Calm and specific beats defensive every time.
Complain effectively and get a resolution
Make a complaint that leads to something being fixed.
Help me make a complaint. WHO TO: [THE ORGANISATION] WHAT WENT WRONG: [WHAT HAPPENED, WITH DATES] WHAT I HAVE ALREADY DONE: [PREVIOUS CONTACT AND THEIR RESPONSE] WHAT I WANT: [THE RESOLUTION YOU ARE SEEKING] WHAT EVIDENCE I HAVE: [RECEIPTS, MESSAGES, PHOTOGRAPHS, NOTES] HOW MUCH IT MATTERS: [minor / significant / has cost me money or caused real problems] CURRENCY: [IF MONEY IS INVOLVED] Produce: 1. WHAT YOU ARE ENTITLED TO ASK FOR - realistically. Complaints succeed when the request is proportionate and specific. Options usually include: the thing fixed, a refund or partial refund, compensation for consequential costs, or an apology and a change of process. Say what is reasonable here, and be honest if what I want exceeds what is likely. 2. THE COMPLAINT LETTER - structured: - Account or reference details at the top - What went wrong, factually, in date order - The impact on me, briefly and concretely - What I have already done and what response I received - The specific resolution requested - A deadline for response Under one page. Long complaints are read less carefully, not more sympathetically. 3. FACTS OVER FEELINGS - the impact should be stated concretely: time lost, money spent, what could not be done. That is actionable. Expressions of how annoying it was are not, and they make the complaint easier to dismiss. 4. THE ESCALATION PATH - the actual route for this kind of organisation: their internal complaints process first, then the relevant ombudsman, regulator, or trade body. Most organisations require their own process to be exhausted before an external body will look at it, so going straight to the regulator usually just returns you to the start. Say what the path is and note it varies by country and sector. 5. THE EVIDENCE - what I have, what strengthens the complaint most, and what is missing that would help. Send copies, keep originals. 6. THE TIME LIMITS - complaints and escalation routes usually have deadlines, and they are frequently missed. Say what to check. 7. THE RECORD - from now on: dates, times, names, what was said, and reference numbers for every contact. If this escalates, the trail is what decides it. 8. THE PHONE VERSUS WRITING QUESTION - writing creates a record and is usually better for a formal complaint. If a call is needed, follow it with an email summarising what was agreed. 9. THE TONE - firm, factual, and not angry. Anger is understandable and reduces the chance of resolution because it shifts the interaction from a problem to be solved to a person to be managed. 10. WHAT TO DO IF THEY REFUSE - the next step, and an honest assessment of whether it is worth pursuing given how much I said this matters. 11. THE PROPORTIONALITY CHECK - the effort a complaint takes against what it achieves. For a minor issue, say plainly if it is not worth the hours.
What you get: A realistic entitlement, a one-page structured complaint, impact stated concretely, the correct escalation path and a record-keeping habit.
Tip: Point 4 saves months. Going to a regulator before exhausting the organisation's own process gets you sent back to the beginning.
Turn feedback into a prioritised action list
Decide what to fix based on what people told you.
Turn this feedback into a priority list. FEEDBACK: """ [PASTE - SURVEY RESPONSES, REVIEWS, SUPPORT TICKETS, OR ALL OF THEM, LABELLED BY SOURCE] """ WHAT I PROVIDE: [CONTEXT] MY RESOURCES: [WHAT YOU CAN REALISTICALLY CHANGE] WHO GAVE THIS FEEDBACK: [AND HOW THEY WERE SELECTED] Produce: 1. THE SOURCE BIAS - each feedback source over-represents someone. Reviews come from the extremes, support tickets from people with problems, surveys from responders. Say what this combined set over- and under-represents. 2. THE THEMES - with counts by source. A theme appearing across several sources is stronger evidence than one concentrated in a single channel. 3. THE CLASSIFICATION - each theme as one of: - REAL PROBLEM: something is broken or missing - EXPECTATION PROBLEM: it works as designed and was described badly. Usually the cheapest to fix. - WRONG CUSTOMER: people who should never have bought or signed up. A targeting problem. - PREFERENCE: someone wants it different, not broken. Changing may make it worse for others. 4. THE VOCAL MINORITY CHECK - for each theme, how many people, out of how many total. One articulate person can make a preference sound like a crisis. State counts everywhere. 5. THE SILENT MAJORITY - for each theme, who is affected but did not write in. Usually a multiple of those who did. Note where the true impact is likely much larger than the feedback volume suggests, and where it is not. 6. THE PRIORITY MATRIX - each theme scored on: how many are affected, how badly, whether it causes them to leave, and effort to fix. Rank by (impact / effort). 7. THE QUICK WINS - expectation problems and documentation gaps, which cost little and remove real friction. 8. WHAT NOT TO CHANGE - requests that would damage the offering for your main audience, come from people outside your target, or would take resources from something that matters more. Name them explicitly, because the loudest requests are frequently in this category. 9. WHAT TO INVESTIGATE - themes where the feedback signals a problem but not its cause. 10. WHAT TO TELL THE PEOPLE who gave feedback, including on the things you will not do.
What you get: Themes classified into real, expectation, wrong-customer and preference problems, with counts, a priority matrix and a do-not-change list.
Tip: Point 3's expectation-problem category usually holds the cheapest wins. A sentence on a product page fixes what looked like a product defect.
Use AI with care in this job
- Never promise refunds, dates or exceptions that policy does not allow.
- Remove customer personal data before pasting tickets into AI tools unless approved.
- Review AI replies before sending until you trust the templates.
Free tools that help
Which AI should you use?
Every prompt here works in the major assistants - ChatGPT, Claude and Gemini - on free or paid plans. For long documents or careful writing many people prefer Claude; for images, voice and everyday tasks ChatGPT and Gemini are strong all-rounders. Models change every few months, so see our AI models guide or answer three questions in the AI Model Picker.
Questions people ask
Can AI answer customers automatically?
Chatbots can handle simple, well-documented questions. For complaints and exceptions, use AI to draft and a person to send.
How do I make canned responses sound human?
Add a slot for a personal detail from the customer's message, and vary the opening. The canned responses prompt builds this in.
How do I build an AI support assistant?
Start with a clear system prompt that sets scope, tone and escalation rules. Try our System Prompt Generator.