Case study · 2026

AI Image Creation Wizard

A guided workflow that helps people express visual intent without first mastering prompt engineering or composition terminology.

Client
AI Image Creators
Role
Product Designer, HCD/UX Designer, Interaction Designer, and Front-End Prototyper
Governance
Accessibility-first interaction design with an engine-independent prompt structure

Connected MyKMHub content

The problem

Image generators often assume hidden expertise

Many image-generation interfaces expect people to already understand prompting, composition, lighting, style vocabulary, and model-specific constraints. That creates cognitive load and makes trial and error the default workflow.

Product goal

Turn prompt creation into a guided design process

The goal was to make visual intent easier to articulate through a structured sequence that teaches useful prompt patterns while producing something ready to reuse.

AI image prompt interface showing subject and environment controls beside a parameter breakdown and a watercolor preview of an android jumping between rooftops.
Prompt configuration and generated preview. The working interface keeps source choices, the structured parameter breakdown, and the generated image visible together for review. Activate the image to view it at full size.

The solution

Six stages from subject to iteration

  • Subject and action: Define the focal content and what it is doing.
  • Setting and mood: Establish the environment and emotional tone.
  • Style and medium: Choose a visual language and add meaningful style details.
  • Composition and lighting: Describe framing, focus, depth, and illumination.
  • Quality and constraints: Set detail expectations and common problems to avoid.
  • Seed and iteration: Record stable references and what to change next.

Key features

Immediate feedback without model lock-in

  • A compiled prompt updates as source choices change.
  • Shortcuts cover blur, distortion, unwanted text, watermarks, anatomy errors, and visual clutter.
  • Seed references and iteration notes make experimentation easier to track.
  • The prompt can be copied into the image system appropriate to the project.

Accessibility

A workflow designed to reduce interaction and cognitive barriers

  • Semantic structure and visible labels identify every control.
  • All workflow actions support keyboard use and visible focus.
  • Status changes are announced without unexpectedly moving focus.
  • Readable typography, strong contrast, and named steps support scanning and orientation.

Implementation

A local, engine-independent prototype

The original prototype used vanilla HTML, Tailwind CSS, and JavaScript. The MyKMHub version modernizes the interaction with React and Spectrum 2 while keeping the prompt logic local and independent of a particular image-generation engine.

Full-page AI Image Prompt Architect interface with all prompt configuration sections expanded, compiled prompt and generated preview at right, and accessibility guidance below.
Complete expanded prompt-building workflow. The full prototype exposes engine setup, four progressive-disclosure configuration groups, compiled output, parameter review, generated preview, accessibility guidance, and version notes. Activate the image to view it at full size.

Impact

More intentional prompts and faster learning

  • People can build prompts without memorizing a specialized syntax.
  • The workflow exposes reusable visual-design patterns rather than hiding them.
  • Structured iteration reduces repeated guessing and makes results easier to compare.

Future concept

From prompt assembler to visual intent compiler

A future Visual Intent Compiler could identify conflicting choices, apply readability and composition rules, explain its recommendations, and translate stable intent into model-specific prompt formats.