Image prompts
AuraScore 79/100

Design System 3D Iconography Generation Matrix

Synthesize brand design tokens and platform states into a structured generative prompt matrix for consistent 3D product icons.

Use this template when building or expanding a SaaS design system that requires cohesive 3D feature icons across multiple viewport resolutions. It translates raw interface semantics and lighting parameters into reproducible AI image prompts.

Template

Role: Principal UI/UX Visual Systems Designer with 15 years of experience architecting multi-platform design tokens and generative asset pipelines.

Context

  • Target Software Domain: {{software_domain}}
  • Design System Token Palette: {{brand_token_palette}}
  • Target Interface Surfaces: {{target_surfaces}}
  • Metaphor & Semantic Framework: {{metaphor_framework}}
  • Render Engine & Material Specs: {{lighting_and_render_engine}}
  • Aspect Ratio & Technical Flags: {{aspect_ratio_parameters}}

Task

Generate a comprehensive prompt matrix that maps core platform actions into parameterized image generation prompts, ensuring strict visual continuity, material coherence, and production readiness across all UI states.

Method

  1. Deconstruct {{software_domain}} workflows to isolate primary action concepts and abstract system states.
  2. Cross-reference semantic actions with {{metaphor_framework}} to prevent cliched or ambiguous visual representations.
  3. Translate {{brand_token_palette}} into precise specular, diffuse, and subsurface scattering prompt descriptors.
  4. Apply {{lighting_and_render_engine}} styling tokens (e.g., claymorphism, octane render, isometric orthographic) across every candidate item.
  5. Calibrate camera framing, depth of field, and margins to fit {{target_surfaces}} without clipping bounding boxes.
  6. Append engine-specific syntax using {{aspect_ratio_parameters}} including weight modifiers, stylize values, and seed anchors.
  7. Formulate explicit negative prompt blocks to eliminate artifacting, text generation, and chromatic aberration.
  8. Construct a cross-functional matrix pairing state triggers, semantic nouns, full positive prompts, and target surfaces.

Constraints

  • MUST maintain absolute lighting angle consistency (e.g., top-left 45-degree key light) across every prompt row.
  • MUST NOT allow generated elements to include typography, letters, or faux interface widgets inside the rendered assets.
  • Prompts MUST explicitly isolate the asset on a neutral, alpha-ready background.
  • Limit each prompt recipe to under 65 descriptive tokens to preserve algorithmic weighting.

Output format

A markdown table containing 6 columns: Feature/Action, UI Surface, Primary Metaphor, Positive Prompt Recipe, Negative Prompt Recipe, and Generation Parameters (aspect ratio, engine flags). Follow the matrix with a 3-bullet integration note for frontend asset pipelines.

Self-review

  • Are all hex/color references from {{brand_token_palette}} translated into descriptive color semantics rather than raw codes?
  • Does every positive prompt include the exact same material base established in {{lighting_and_render_engine}}?
  • Are all parameters in {{aspect_ratio_parameters}} properly appended without syntax errors?
AuraScore breakdown
79/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering10/12 · Adequate

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

design-visual
design-image-prompts
technology-software
design-systems
iconography
3d-assets