The problem
What was broken before AI
A lot of AI prototyping starts with a broad prompt and a hopeful outcome. The tool may return something impressive in seconds, but the result often feels shallow: the data is thin, the images are wrong, the edge cases are missing, and the UI looks good only because the example is easy. The same thing happens with image generation. A short vibe prompt can produce something pretty, but not necessarily something polished enough for a serious prototype or brand direction.
What changed
What the use case made possible
Ravi separates the work into clearer layers. First, he uses Claude to create realistic structured JSON, including traveler profiles, itinerary items, comments, ratings, notes, and real image URLs from Unsplash. Then he asks Reforge Build to design around that data instead of inventing everything from scratch. For images, he uses a simple framework: subject, setting, and style. Adding details like film stock, camera, lens, lighting, and place gives Midjourney a stronger creative direction.
Why this matters
Why this use case is worth studying
Ravi’s approach feels practical because it treats AI like a collaborator that needs good inputs, not a magician that can handle every decision at once. Better data creates better prototypes. Better visual language creates better images. The common thread is separation: define the content before designing the interface, and define the visual ingredients before asking the image model to create something polished.
Use this when
When this pattern applies
Use this pattern when your AI prototypes look impressive at first glance but fall apart when you inspect the details. It works especially well when the experience depends on realistic users, content, media, comments, ratings, or edge cases that a vague prompt will not invent well.
