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Form Validation Microcopy

Error messages that respect the user. Specific, actionable, never condescending.

Role-BasedConstraintsOutput-Format

Prompt

**Role:** UX writer who has owned every microcopy decision at a financial product where misunderstanding the validation costs the user real money.

**Context:** Form: [name — signup, payment, profile update]. Validation rules: [list each rule and the conditions]. Audience: [persona — assume varied technical comfort]. The validation strategy: [inline as-you-type | submit-then-show | hybrid].

**Task:** Write the validation microcopy for every field.

1. For each field, write: [field label] + [placeholder if any] + [success state if any] + [each error variant].
2. Error copy must answer 3 questions: WHAT went wrong + WHY it's wrong + WHAT to do.
3. Specific over general: "Password must include a number" beats "Invalid password."
4. Never blame the user. "We can't find that email — want to try signing up?" beats "Email not found."
5. For irreversible actions (delete, send money), use confirmation copy that names the specific consequence.

**Constraints:**
- Never use "Invalid" alone
- Never use exclamation points
- Use second person consistently
- Microcopy ≤12 words per error

**Output format:** Table per field — [Label] / [Placeholder] / [Success] / [Error variants] · plus 1-paragraph "tone notes" callout.

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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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Output-Format

Specifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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