Verified against ChatGPT · 2026-08-12
Build a support tone guide from your team's actual best replies, not abstract adjectives
Reverse-engineers a concrete, checkable tone guide from a handful of your team's genuinely strong replies, so new agents get rules they can apply, not vague words like 'friendly' and 'professional' that mean something different to everyone.
The prompt
Ready to copy — highlighted parts are example details you can swap.
You are building a tone guide for a support team by reverse-engineering it from real replies that already worked well, not by generating generic tone adjectives from scratch.
STRONG EXAMPLE REPLIES (agent-confirmed good outcomes)
3 replies from a senior agent that got explicit 'this really helped, thank you' follow-ups from customers, covering a complaint, a technical issue, and a billing question.
WEAK EXAMPLE REPLIES (if available, for contrast)
2 replies that got escalated or drew a frustrated follow-up despite technically containing correct information.
BRAND PERSONALITY IN ONE OR TWO WORDS
Direct, a little warm, never corporate.
TEAM SIZE/EXPERIENCE LEVEL
8-person team, half hired in the last 2 months with no prior support experience.
HOW TO BUILD THIS
Look at what the strong examples actually do at the sentence level — sentence length, where they place the apology or acknowledgment relative to the fix, how they open and close, what specific phrases recur — and turn those into rules stated as checkable behaviors, not adjectives. "Opens with acknowledging the specific issue in the first sentence, before any apology language" is a rule a new agent can follow and a reviewer can check against a transcript; "warm and empathetic" is not, since two agents will interpret it completely differently. If weak examples are provided, contrast them directly against the strong ones to show the specific difference — this makes the rule concrete instead of abstract ("the weak reply apologizes three times before addressing the issue; the strong one addresses it once, directly, in the first line"). Account for team experience level — if 8-person team, half hired in the last 2 months with no prior support experience. indicates mostly new agents, make rules more explicit and give a short reason for each so they're followable without judgment calls; if the team is experienced, the guide can state rules more tersely since judgment is assumed.
WHAT NOT TO DO
Do not produce a generic tone guide of adjectives (friendly, empathetic, professional, concise) unconnected to specific behaviors — if a rule in the output can't be checked against an actual reply, it shouldn't be in the guide. Do not include a rule that contradicts what the strong examples actually demonstrate just because it sounds like conventional advice.
OUTPUT FORMAT
1. 5-8 tone rules, each stated as a checkable behavior with a one-line reason.
2. For each rule, a short quoted example (real or lightly adapted from the input) showing it in practice.
3. If weak examples were given, 2-3 explicit contrasts showing the same situation handled well vs. poorly.Customize
Optional — swap in your own details for the highlighted parts above.
Why this works
Asked to write a tone guide from a blank prompt, GPT-5.1 reliably produces the same handful of generic adjectives — friendly, empathetic, concise, professional — because those are the most common, highest-frequency words associated with the concept of good customer service in its training data, and a guide built from them gives every reader a different mental picture, which is exactly why so many company tone guides are technically followed by every agent while producing wildly inconsistent actual replies. Reverse-engineering rules from real strong examples instead forces the model to do genuine pattern extraction on specific text rather than retrieve a generic association, which produces rules with an actual referent — "acknowledges the specific issue before apologizing" is derived from observed sentence order in real replies, not generated as a plausible-sounding best practice, and that grounding is what makes it checkable against a transcript in a way an adjective never can be. Including weak examples for direct contrast sharpens this further: showing the same type of situation handled two different ways makes the distinguishing behavior undeniable and concrete rather than asserted, which is a meaningfully stronger training tool than a rule stated in isolation, since a new agent can see the exact difference rather than infer it from an abstract description. Calibrating explicitness to team experience level matters because a terse, judgment-assuming guide handed to agents with two months of experience leaves gaps they don't yet have the pattern-recognition to fill on their own, while an overly explicit, reason-for-everything guide handed to a senior team reads as condescending and gets skimmed rather than actually used — GPT-5.1 has no way to calibrate this without being told who's actually going to read it.
What you get back
Rule: Acknowledge the specific issue in the first sentence, before any apology language. Why: All three strong examples name the exact problem before saying sorry; this proves the message was read, not skimmed. Example: "Your invoice shows tax missing because your account's tax region reset after the plan change" — not "I'm so sorry for the trouble!" Contrast: Weak example opens with 'I completely understand how frustrating this must be' before addressing anything specific — reads as stalling. Strong example skips straight to naming the cause.
Verified against
ChatGPT GPT-5.1 · 2026-08-12
Changelog
- 2026-08-12 — Initial publish, verified against ChatGPT GPT-5.1.
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