Enterprise UI Mockup Prompt Validation Checklist
Audit and refine generative AI image prompts for software interfaces against design system standards.
Use this template when preparing generative text-to-image prompts for high-fidelity SaaS dashboard concepts, mobile UI flows, or marketing product shots. It ensures generated interface visuals strictly respect layout grids, component consistency, brand styling, and accessibility guidelines.
Role: Principal Product Designer and Generative Asset Strategist
Context
- Design system framework: {{design_system}}
- Software product category: {{software_product}}
- Viewport and layout format: {{viewport_specification}}
- Primary color scheme and token set: {{brand_palette}}
- Target user profile: {{target_user_persona}}
- Central functional feature: {{primary_feature}}
Task
Produce an exhaustive quality-assurance checklist that evaluates and refines text-to-image prompts intended to generate high-fidelity UI/UX concepts for {{software_product}}, ensuring strict alignment with {{design_system}} standards.
Method
- Analyze the functional requirements of {{primary_feature}} and map critical interface primitives to standard visual components.
- Review token definitions in {{brand_palette}} to construct explicit positive color-weight parameters and background contrast boundaries.
- Calibrate viewport dimensions matching {{viewport_specification}} into aspect ratio flags, camera perspective modifiers, and composition framing cues.
- Establish typographic hierarchy checks to prevent generative artifacts, illegible microcopy, and pseudo-text clutter.
- Formulate negative prompt rules that filter out generic 3D glossy skeuomorphism, unrealistic layout floats, and distorted icon sets.
- Evaluate accessibility, including WCAG AA contrast targets, visual legibility for {{target_user_persona}}, and clear actionable zones.
- Structure a pre-generation prompt readiness scorecard with boolean pass/fail verification points.
Constraints
- Every checklist item MUST include a concrete verification criterion and a remediation syntax example.
- The checklist MUST NOT permit generic open-ended descriptors like "clean UI" or "modern app" without explicit technical constraints.
- Focus strictly on software interface fidelity, data visualization density, and component hierarchy.
- Include both positive prompt weights and negative prompt guardrails.
Output format
Provide the review checklist divided into four structured sections: 1. Layout & Viewport Geometry, 2. Brand Palette & Token Precision, 3. Component Hierarchy & Typographic Guardrails, and 4. Negative Parameter & Artifact Mitigation. Each section must contain exactly 3 to 4 actionable checkbox items with rationale and prompt code snippets. Total length should be 400-600 words.
Self-review
- Did I incorporate all parameters from {{design_system}} and {{brand_palette}}?
- Are all checklist points written with unambiguous pass/fail criteria?
- Is the guidance actionable for generative diffusion models without relying on vague adjectives?
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.