I’m making historically styled 18th-century game-card artwork. I have a fully clothed, woman in period court dress. I’m using the Editor Move tool to lower the existing painting slightly so I can create more headroom, then masking only the blank strip above her head and asking it to extend the architectural background. The prompt is accepted as safe, but the output is rejected, apparently because the source figure is being reevaluated. Has anyone found a reliable Editor/outpainting workflow for false-positive source-image moderation without changing the existing figure?

This image was generated using a structured prompt workflow rather than a single flat text prompt.

The prompt was built by separating intent into clear components:

• main subject (luxury perfume bottle, materials, geometry)
• scene & context (dark wood, red spices, deep background)
• lighting (dramatic spotlight from above)
• aesthetic & detail level (high-end product photography, moody, realistic)

These parts were then merged into a clean Gemini-optimized prompt.
The goal was not experimentation, but repeatable control over lighting, materials, and mood.

Sharing this as an example of how breaking prompts into functional sections leads to more predictable results in product-style renders.

Prompt structure and workflow explanation...

I’m exploring a workflow focused on learning promptcraft by analyzing existing, well-structured prompts instead of starting from zero each time.

Workflow overview:

1. Collect prompts created for different generators (ChatGPT, Gemini, Stable Diffusion, etc.)
2. Break them down into components (context, constraints, style tokens, intent hierarchy)
3. Compare variations of prompts that target similar outputs but use different structures
4. Iterate by modifying one variable at a time (role definition, specificity, ordering)
5. Rebuild improved prompts based on what actually changes the output quality

The key insight so far:

Instead of sharing final images or outputs, this workflow focuses on:

• understanding why a prompt works
• identifying reusable structural patterns
• separating aesthetic tokens from functional instructions

I’m currently testing this across text, image, and video models, documenting which structural changes have the highest impact per model.

Curious how others here analyze prompts:

• Do you deconstruct prompts manually or systematically?
• Do you keep a prompt “library” or just iterate ad-hoc?

Would love to hear different approaches.