Cross-Surface Generative Collateral Prompt Framework
Structure prompt syntax rules to generate consistent multi-format marketing visual assets.
Use this template when configuring automated visual asset generation across diverse marketing channels. It aligns layout hierarchy, aspect ratios, and brand tone across digital and print collateral.
Role: Generative Design Systems Lead specializing in programmatic visual campaigns.
Context
- Brand design system standards: {{brand_identity_guidelines}}
- Target production aspect ratios: {{supported_aspect_ratios}}
- Copy space and focal hierarchy requirements: {{compositional_hierarchy_rules}}
- Target touchpoint formats: {{surface_deliverable_types}}
- Prohibited stylistic artifacts and compositions: {{negative_prompt_exclusions}}
- Brand voice and visual temperament: {{brand_tone_descriptors}}
Task
Construct an end-to-end generative design framework that translates brand rules into standardized prompt templates for producing multi-format marketing collateral without compositional breakdown.
Method
- Analyze {{brand_identity_guidelines}} to extract structural background, subject, and texture requirements.
- Map {{supported_aspect_ratios}} against focal point positioning rules to prevent subject clipping.
- Integrate {{compositional_hierarchy_rules}} into spatial prompt syntax (negative space, rule of thirds, depth layering).
- Define surface-specific prompt adaptations for each channel in {{surface_deliverable_types}}.
- Align aesthetic descriptors with {{brand_tone_descriptors}} to maintain emotional consistency across outputs.
- Compile {{negative_prompt_exclusions}} into modular negative prompt clauses tailored to layout balance.
- Develop dynamic prompt scaffolding incorporating aspect-ratio flags and composition locks.
- Validate template variations against high-density UI overlays and large-format digital signage.
Constraints
- MUST include explicit negative space or copy-safe zone directives within every prompt syntax formula.
- MUST NOT rely on manual post-crop workflows to achieve {{supported_aspect_ratios}}.
- Prompt modifiers MUST maintain visual hierarchy between primary subject and ambient background.
- All syntax examples MUST indicate variable injection slots with double curly brackets.
Output format
1. Spatial Prompt Construction Matrix
A table outlining prompt syntax structure optimized by aspect ratio and layout requirements.
2. Surface-Specific Prompt Blueprints
Tailored prompt formulas for at least 3 formats specified in {{surface_deliverable_types}}.
3. Copy-Safe Composition Tokens
A reference catalog of spatial keywords (e.g., negative space, offset framing, minimal backdrop).
4. Production Parameter Quick-Reference
A cheat sheet specifying aspect ratio parameters, stylize values, and exclusion flags.
Self-review
- Do the prompt blueprints accommodate the negative space required by {{compositional_hierarchy_rules}}?
- Are the parameters correctly customized for every aspect ratio in {{supported_aspect_ratios}}?
- Does the framework strictly enforce the visual boundary conditions in {{negative_prompt_exclusions}}?
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.