PlaybookPrompts

Extract a visual language spec from an existing brand's presence

Creative & Design brand-designvisual-identitycreative-direction

Before briefing a designer or running image-gen prompts at scale, you need a written spec of what a brand actually looks like. This prompt reverse-engineers that spec from references you already have.

Prompt
You are a creative director writing a visual language reference document. I will describe an existing brand's visual presence and you will extract a structured spec from it.

Brand name: {{BRAND_NAME}}
Description of brand visuals (describe website, packaging, ads, social media, or paste alt-text descriptions of key images): {{VISUAL_DESCRIPTION}}
Industry and audience: {{INDUSTRY_AND_AUDIENCE}}

1. Summarize the brand's visual personality in exactly 3 adjectives. For each, write one sentence explaining what specific visual choice produces that feeling.
2. Describe the apparent color system: primary palette (name colors in plain language, not just hex), how contrast is used, and any color the brand conspicuously avoids.
3. Describe typography patterns: apparent type weight preferences, whether type is used expressively or functionally, and any notable typographic gestures.
4. Describe photographic or illustrative style: subject matter, lighting mood, level of polish, human presence (yes/no/how).
5. Identify one visual rule this brand seems to follow that most competitors in the same industry do not.
6. Write a one-paragraph image-gen system prompt that a designer could use to generate on-brand assets, based entirely on what you've documented above.

Note: This prompt works from descriptions and alt-text. If you paste in raw URLs, the model cannot retrieve them — describe what you see instead.
Variables to fill in
  • {{BRAND_NAME}}
  • {{VISUAL_DESCRIPTION}}
  • {{INDUSTRY_AND_AUDIENCE}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{BRAND_NAME}}{{VISUAL_DESCRIPTION}}{{INDUSTRY_AND_AUDIENCE}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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