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Japanese Team Analysis Infographic
UI / UX Design

Japanese Team Analysis Infographic

GPT Image 2 Japanese team member analysis infographic with radar charts and cute avatars

Prompt

Create a Japanese-language infographic titled "うちの部署のメンバー スペック分析" (Our Department Member Spec Analysis) with subtitle "個性豊かなプロ集団(たぶん)". Layout as a 2x3 grid of six member cards on a clean white background with pastel accents and star decorations. Each card features a cute chibi-style cartoon avatar and includes: member name and role in Japanese, a radar chart or bar chart showing their stats, bullet-point strengths and weaknesses in Japanese. Add a summary section at the bottom with overall team evaluation, a team compatibility graph placeholder, and a final takeaway note. Cheerful office illustration style, soft rounded UI elements, kawaii aesthetic, highly legible Japanese typography, no watermark.

How to use this prompt

1
Review the full prompt

Read the complete Japanese Team Analysis Infographic prompt and identify the subject, style, camera, lighting, and composition requirements before generating.

2
Customize variables

Replace bracketed or argument-style placeholders with your product, character, brand, scene, color palette, or aspect ratio requirements.

3
Generate and iterate

Open https://www.gptimagehub.com/generate?promptId=cmqf0pcs3000313a7ef8i55ux, generate the image, then refine the prompt with more specific subject, text, layout, or negative constraints if needed.

Prompt FAQ

What is the Japanese Team Analysis Infographic prompt best used for?

This prompt is best used for ui / ux design images where you want a reusable structure, detailed visual direction, and consistent output quality.

Can I edit the prompt before generating?

Yes. The full prompt text is visible on this page so you can change subjects, product names, colors, composition, camera terms, aspect ratio, and style notes before generation.

Which model should I use with this prompt?

Use the model shown in the prompt metadata as the default starting point. If another image model supports the same aspect ratio and instruction style, you can adapt the prompt and compare results.

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