Use case: Model comparisons, packaging tests, poster prompts, UI screenshot prompts, and image-generation QA.
Create a text-rendering stress-test board for an image generation model.
Use exactly this target text:
{{target_text}}
Create four panels:
1. large poster headline
2. small product label
3. curved sticker text
4. UI button or app card
Rules:
- keep the target text short
- render the same exact text in every panel
- label each panel clearly
- use a clean comparison-board layout
- avoid adding unrelated words
- make spelling accuracy more important than decorative complexity
Output goal:
A clear board that shows whether the model can preserve exact text across multiple realistic design contexts.
What to customize first
target text
panel count
design contexts
layout style
evaluation labels
How to use this template responsibly
This prompt is meant to be adapted into a brief for a real task, not copied into a model without
context. Start with the use case, then fill in the variables, run the quality checks, and keep the
source signal separate from your final prompt variant.
Decision
Use this page for
Do not skip
Task fit
Model comparisons, packaging tests, poster prompts, UI screenshot prompts, and image-generation QA.
Confirm the output will be reviewed by a person before reuse.
Variables
target text, panel count, design contexts
Replace placeholders with concrete details from your own brief.
Quality bar
Every panel should contain the exact target text.
Compare the result against the checklist, not only against taste.
Failure prevention
The model paraphrases the target text.
Rewrite the prompt if the first run exposes this failure.
Why this prompt works
Text accuracy needs controlled tests. Using the same phrase across contexts reveals whether failures come from size, curvature, or UI-like rendering.
Evaluation workflow
Use this page as a repeatable prompt test, not a one-off prompt dump. Save the exact prompt
version, model name, input references, and output settings before comparing results. Then judge
the output against the checks below so the decision is based on observable behavior instead of
whether the first image, video, page, or workflow looks impressive at a glance.
Run the unchanged template once to establish a baseline for the model and task.
Replace the variables with concrete details from your brief, audience, product, or review case.
Score the result against the first quality check before judging style or novelty.
If the first failure mode appears, rewrite the constraints before increasing generation volume.
Keep the best output and rejection notes together so future prompt changes can be compared fairly.
Rewrite record
Before saving this prompt as a team asset, write down what changed from the template and why. The
useful record is not only the final prompt text; it is the task, variables, model, source signal,
quality checks, failure notes, and rejected outputs that explain why this version is trusted.
Record which variables were changed from the public template.
Note whether the output is for exploration, internal review, or external publication.
Keep the first failed result if it reveals a useful constraint for the next version.
For client or brand work, keep rights, claims, likeness, and policy review separate from visual taste.
Quality checks before using the output
Every panel should contain the exact target text.
Panel labels should be readable.
Decorative style should not hide spelling errors.
Common failure modes
The model paraphrases the target text.
Small labels become illegible.
The board changes too many variables at once.
Originality and reuse boundary
The source signal explains why this pattern is worth watching, but the value of this page is the
rewritten structure, variables, quality checks, and failure analysis. Treat the final prompt as your
own working brief only after you have changed the subject, constraints, review criteria, and output
context for your own task.
Do not republish source creator text as if it were your own prompt.
Keep a record of the final prompt variant and the model used.
Use the failure modes to decide whether another model, reference image, or manual edit is needed.
For commercial work, review rights, brand claims, likenesses, and policy-sensitive content before publishing.