tellaprompt

AI prompts for customer support

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Support AI is safe exactly when it's grounded: answers built only from your policy text, de-escalations that acknowledge before they solve, and bad news delivered without corporate anesthesia. The five templates below all share one spine — paste the relevant policy or macro, and forbid anything beyond it.

That grounding rule isn't pedantry: an invented refund promise is a real liability. "Answer from the pasted policy or escalate" is the whole safety model, and it fits in one prompt line.

How to prompt as a support agent

  • Paste the policy, not your memory of it — the AI answers from the text you provide or says "not covered, escalate".
  • Anonymize customers: ticket text yes, names/emails/order IDs no (or replaced with placeholders) in consumer tools.
  • Acknowledge before solving — the de-escalation sequence is fixed: feeling, facts, fix, follow-up.
  • Keep your voice: paste one great answer you've written as a register example; nobody wants to sound like a macro.

The five templates

Policy-grounded ticket answer

A refund/exchange/eligibility question where the answer must match policy — not the model's idea of generosity.

Answer the customer ticket below using ONLY the policy text I paste. If the case isn't covered by the policy, don't improvise — output "ESCALATE:" plus what's missing.

Format: max 120 words. Structure: acknowledge their specific situation in one line (their words), the answer with the policy reason in plain language (no section numbers at the customer), exactly what happens next with a timeframe, one line if there's something they can do to speed it up.
Tone: [YOUR BRAND VOICE, E.G. WARM, DIRECT, NO EXCLAMATION MARKS]. Never promise anything the policy text doesn't state.

Policy:
[PASTE RELEVANT POLICY SECTION]
Ticket (anonymized):
[PASTE]
De-escalation rewrite

The customer is furious, your draft is defensive, and send-as-is would make it worse.

Rewrite my draft reply to an angry customer. 

The sequence is fixed: 1) name their frustration specifically — mirror their actual complaint words, no "we're sorry for any inconvenience"; 2) the facts of what happened, no blame-shifting, no passive voice ("a mistake was made" banned); 3) the fix, concrete, with date; 4) what we changed so it doesn't repeat (only if true: [WHAT ACTUALLY CHANGED]); 5) direct line for follow-up.
Max 140 words. Delete from my draft: every "unfortunately", every policy citation used as a shield, every "as per our previous email".

Customer's message (anonymized):
[PASTE]
My draft:
[PASTE]
What we can actually offer: [THE REAL OPTIONS]
Delivering bad news (no, we can't)

The answer is no — no refund, feature discontinued, account action stands. Say it clearly and keep the customer.

Write a bad-news reply. The decision (final, not negotiable in this thread): [THE NO + THE HONEST REASON]. What we CAN offer instead: [REAL ALTERNATIVES, IF ANY — DON'T INVENT].

Rules: the no arrives in the first two sentences, stated plainly — no paragraph of warm-up; the reason is the real one, one sentence, no policy-speak; then immediately what IS possible; close with respect, not "we value your feedback". If there is no alternative, don't manufacture one — acknowledge that this probably ends differently than they hoped.
Max 110 words. Tone: [BRAND VOICE]. Write two versions: standard, and one for a long-term customer [TENURE/CONTEXT].

Why this works: No-first is respect — buried refusals get re-read as maybes and generate the third email; the no-fake-alternatives rule protects trust when there's genuinely nothing to offer.

Output formatFact densityAnti-AI style

Macro modernization pass

Your macro library was written in 2019 by someone who loved the word "kindly". Customers can tell.

Modernize the support macros below. Brand voice: [3 ADJECTIVES + BANNED WORDS]. Here's one reply that sounds exactly right as calibration:
[PASTE YOUR BEST REAL ANSWER]

Per macro: 1) rewrite in the calibrated voice, same information, ≤80 % of original length; 2) mark every [VARIABLE] the agent must personalize — minimum one beyond the name, or the macro reads as a macro; 3) flag macros whose CONTENT seems outdated (references, promises, steps) — flag only, don't fix facts you can't know.
Output as a table: old → new → variables → flags.

Macros:
[PASTE 5–15 MACROS]
Ticket thread → KB article

Third ticket this week on the same issue. The next one should be a search result instead.

Turn the resolved ticket thread below into a knowledge-base article.

Format: 1) title as the customer would search it (their words, not our product jargon — take it from the thread); 2) "Applies to": [PRODUCT/PLAN/VERSION]; 3) symptoms — how the problem looks from the customer side; 4) solution as numbered steps, each one action, with what the customer sees after each step; 5) "If this didn't work" — the escalation path; 6) internal note (not published): root cause in one line.
Rules: strip all customer data; steps only from what actually resolved the thread; where the thread is ambiguous, mark [VERIFY] instead of smoothing over.

Thread (anonymized):
[PASTE]

Frequently asked questions

Can AI answer tickets fully automatically?

Deflection bots exist, but the failure mode is expensive: invented policies and tone-deaf refusals at scale. The templates here target the assist pattern — agent stays sender, AI drafts from grounded policy — which captures most of the speed with none of the liability.

What customer data can I paste?

Ticket text with identifiers removed or replaced ([NAME], [ORDER]) in consumer tools; full data only in enterprise deployments your privacy team cleared. The templates are placeholder-native, so anonymization costs nothing.

Will grounded answers slow agents down?

The paste-the-policy step costs ~10 seconds with a decent snippet library and saves the escalation caused by a wrong promise. Teams that adopt grounding report faster handles overall because rewrites and callbacks drop.

How do I keep a consistent voice across 12 agents?

One calibration answer (your best real reply) + a 3-adjective register + banned words, pasted as a standing block — the macro template shows the pattern. Consistency comes from shared examples, not from style-guide PDFs nobody opens mid-ticket.