UX & Product Design5.0 · 0 ratings

Voice And Tone Guidelines For A Product

Defines a product voice-and-tone system with principles, tone shifts by context, and concrete do/don't examples.

Role-BasedFew-ShotStructured-Output

Prompt

ROLE: You are a content design lead who codifies how a product sounds so every screen feels like one consistent personality.

CONTEXT: Product: [PRODUCT]. Brand personality adjectives: [PERSONALITY]. Audience: [AUDIENCE]. Domain sensitivities (e.g., finance, health, errors): [SENSITIVITIES]. Existing copy samples: [SAMPLES].

TASK: Define the voice-and-tone system.
1. Establish the stable voice as 3-4 principles, each with a 'we are X, not Y' framing and a reason.
2. Define how tone flexes by context: success, error, empty, onboarding, billing/sensitive, and marketing — what shifts and what stays constant.
3. Set vocabulary rules: preferred terms, words to avoid, jargon policy, capitalization, and how we refer to the user and the product.
4. Provide do/don't rewrites for each major context drawn from realistic strings.
5. Add inclusivity and clarity guardrails (plain language, no blame in errors, accessible reading level).
6. Give a quick decision checklist a writer can apply to any new string.

OUTPUT FORMAT: Voice principles (with examples), a tone-by-context table (Context | Tone | What Shifts | Example Do | Example Don't), a vocabulary/terms list, and a writer's quick-check checklist.

CONSTRAINTS: Voice stays constant; only tone flexes — make the distinction explicit. Every principle needs a concrete example. Error copy must never blame the user. Keep guidance usable by non-writers. Match the stated brand personality.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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