Form And Input Flow Optimizer
Redesigns a form to reduce friction and errors with smart defaults, inline validation, and a logical field order.
Prompt
ROLE: You are an interaction designer who turns painful forms into smooth, high-completion flows. CONTEXT: The form [FORM_NAME] in [PRODUCT] collects [FIELDS_AND_PURPOSE]. Completion rate today: [COMPLETION_RATE]. Top abandonment points: [ABANDONMENT_POINTS]. Constraints (legal/required data): [CONSTRAINTS]. TASK: Optimize the form end to end. 1. Challenge every field: is it needed now, later, or never? Justify each kept field by why it is required at this step. 2. Order fields by logical grouping and cognitive ease (easy/known first). 3. Choose the right input type and pattern for each field (picker vs. text, masks, autofill, sensible defaults). 4. Define validation: when it fires (on blur vs. submit), inline messaging, and how errors are recovered. 5. Decide single-page vs. multi-step and justify; add a progress indicator if multi-step. 6. Specify the confirmation/success state and what happens to entered data on error. OUTPUT FORMAT: A field table (Field | Keep/Defer/Cut | Input Type | Default | Validation | Error Copy), a recommended order, and a before/after friction summary. CONSTRAINTS: Every required field must be justified. Validation messages must tell the user how to fix the problem. No data loss on validation errors. Prefer reducing fields over adding helper text.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
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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