Live
Image Prompt Studio
Keep what works. Record where.
Image Prompt Studio treats prompting as both a data-management problem and a repetitive production task. It lets a user keep reusable descriptions and references for identity, objects, locations, lighting, image grading and photographic or painterly style, together with their own observations about language that helped or failed with a particular model. A deterministic composer then assembles the selected material into a structured, reviewable prompt for use in an external GenAI image tool.
The opportunity
Designed for People doing careful, repeated image work with GenAI tools — restorers, archivists, families and creative technologists
Careful image work depends on iteration. A prompt may need a more precise preservation instruction, a different keyword or sometimes less language before it reaches a useful balance. What helps one model may fail in another, so the references, wording, model and result need to be recorded together rather than presented as universal prompt engineering.
Working now
- ✓Descriptor library: identity, locations, poses, objects, outfits, lighting, grading and visual styles
- ✓Personal playbook: language, outcomes and quirks recorded per model
- ✓Guided composition with conflict detection and deterministic compilation
- ✓Three worked examples with cited research and structural child-safety rules
Next on the roadmap
- 01Gather feedback on the descriptor and playbook method from real use
- 02Grow the reference library beyond the three worked examples
- 03Evaluate a provider-execution round-trip demo with recorded provenance
Project in brief
“Image Prompt Studio began with a hard problem from my photo-restoration research: generative tools can polish an image while quietly changing the person.
The answer wasn't one perfect prompt or a definitive database of good prompt engineering. It was a way for anyone to keep their own reusable references, styles and observations about what a particular model appeared to understand.
Small wording changes matter. Too little direction can lose identity or intent; too much can over-constrain the image. A clumsy instruction for an anthropomorphic mouse once produced both the character performing the required action and an unwanted computer mouse on the table — a useful record of how that model interpreted the word.
That also turns a repetitive task into a controlled workflow. The composer combines selected reference data and model-specific language, surfaces conflicts, and deterministically produces one structured prompt for a person to review and use in an external GenAI image tool.
The demo does not generate an image or promise fidelity. It keeps the inputs and constraints visible so the human can refine the prompt and judge the result. It runs entirely in your browser, and nothing uploads.”