Prompt Framework
A practical structure for writing better prompts with clearer subject, style, technical detail, and output requirements.
Prompt Engineering Playbook
English Prompt Guide
A continuously updated English resource for GPT-Image-2 prompt frameworks, reusable templates, practical workflows, and public case-study references.
A few common entry points:
A practical structure for writing better prompts with clearer subject, style, technical detail, and output requirements.
Useful starting points for ecommerce, branding, infographics, UI recreation, realistic photography, editing, and character work.
Shortcuts for realism, text rendering, reference-based editing, and more stable commercial outputs.
The Chinese site has the most complete case-study archive today, while this English section will keep expanding with new prompt pages and guides.
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Public article references
The Chinese site currently contains the full image-heavy breakdowns. This English page focuses on reusable workflows, prompt patterns, and a clean entry point for international visitors.
Turn scattered GPT-Image-2 knowledge into a reusable English reference page.
Prompt frameworks, practical scenarios, advanced tips, and links back to the full Chinese archive.
More English prompt pages, more case-study translations, and more search-friendly navigation.
GPT-Image-2 is especially strong when you need polished ecommerce covers, product retouching, marketplace images, and commercial layouts.
A good workflow is often: reference image -> first draft -> selective edit -> final commercial polish.
It can extend a single product or brand concept into multiple output formats while preserving visual identity.
Useful for posters, carousel covers, brand kits, campaign images, packaging, and launch visuals.
One of the most practical strengths is structured information design: titles, labels, modules, icons, and layout hierarchy.
Modular sections + rounded information boxes + clear heading hierarchy is a reliable starting pattern.
For many real workflows, editing from an uploaded image is more stable than prompting from scratch.
Best use cases include replacing text, adapting layouts, retouching products, and keeping character or pet identity consistent.
If you want the full image-wall style archive with article-by-article breakdowns, visit the Chinese version. If you want a cleaner English entry point, keep this page bookmarked as it grows.
Yes. The page loads the English README from the same directory first, then falls back to GitHub raw if needed.
Yes. The Chinese site currently has the most complete case-study archive and image-wall breakdowns.
It gives international visitors a cleaner entry point, reusable English prompts, and a lightweight guide to the broader project.
Recommended links around this project: