Typographic Hierarchy in Generated Assets

Typographic hierarchy — the visual ranking of text by importance — is something humans read instinctively and AI models implement unpredictably. You know that a headline should be larger, bolder, and more prominent than body text. The model knows this too, in a general sense, but its execution varies wildly. Tell a model to "include a headline and subheading" and you might get a clear hierarchy, or you might get two text elements at identical sizes competing for attention. Brand systems require predictable hierarchy, which means encoding your hierarchy rules into prompt language that the model can follow consistently.

1. Describe Size Relationships Instead of "add a headline and subheading," describe the visual relationship: "a large, bold headline dominating the upper third of the frame with a smaller, lighter subtitle beneath it." Spatial and size language gives the model concrete layout instructions.
2. Specify Weight Contrast Use weight language: "heavy bold headline with thin, light supporting text." The contrast between heavy and light weights is one of the strongest hierarchy signals in typography, and AI models respond well to explicit weight descriptions.
3. Position for Scanning Describe where each text element sits in the composition: "headline centered in the upper third, details aligned left in the lower quarter." Positional language prevents the model from placing text randomly and creates the visual flow that makes hierarchy work.

For recurring brand assets — social templates, presentation slides, marketing headers — document your hierarchy rules as prompt fragments that get reused across generations. "Bold uppercase headline at 60% frame width in the upper third, light sentence-case subtitle at 40% width directly beneath, 20% vertical spacing between elements." This fragment encodes your hierarchy decisions once and applies them to every generation that uses the template.