Fix generic v0 UI
You shipped with v0, Cursor, or Lovable. The screens looked finished in the demo. Then you used it like a real product and it felt like every other AI-generated interface: soft purple accents, identical rounded cards, spacing that wanders, and a type scale that treats everything the same.
That is a generic wall. It is not a prompt problem. It is a design problem.
My v0 app looks generic. What is going on?
AI UI tools optimize for something that looks complete fast. They scaffold screens, fill layouts, and invent components under deadline pressure. What they do not do: establish hierarchy, define a shared type scale, name tokens, or design the empty and error states that make a product feel coherent.
When every screen uses the same card, the same padding, and the same type size, nothing stands out. The product reads as slop, even when the features work. People trust interfaces that show what matters first. Pixels without hierarchy feel like a template of a template.
Why regeneration will not fix it
Generating more screens without rules multiplies the mess. One screen gets a new button style. Another invents a different card radius. A third uses hardcoded spacing that drifts from the rest. The next feature adds a fifth purple.
Tools are good at scaffolding. They are weak at shared systems, named tokens, and the cut list that stops the next squad from reinventing a pattern. Demo pretty and product messy is what happens when you polish one screen and let the rest drift.
How do I fix Cursor or Lovable UI before I generate more?
Stop generating. Run this cleanup pass first:
1. Inventory screens. List what exists. Kill orphans. If a screen does not belong to a live journey, delete it.
2. Kill duplicate patterns. One primary button style. One card. One form layout. Pick the clearest version of each and standardize.
3. Name tokens. Stop hardcoding color, space, radius, and type. Name them once (in CSS variables or a config file) so the next screen inherits the rules.
4. Define states on purpose. Empty, loading, error, success, no permission. AI tools skip these because they are not in the happy-path prompt. Design them deliberately before you ship.
5. One type scale. Fewer sizes, clearer ranks. A product does not need twelve type sizes. It needs three to five that show priority.
Do that cleanup once. Then generate inside the rules. More AI without constraints is not iteration. It is churn.
When to stop DIY
If the product is live and users stall on journeys that should convert, a cleanup pass is not enough. You need Product Design that maps what people do, strips ceremony, and rebuilds hierarchy so first-timers and power users both move.
If AI or agents are part of the product and trust breaks when the model is wrong, AI Product Design for disclosure, undo, and recovery patterns that are not a chat widget as a strategy.
If you cannot name what is broken but you know the interface is the constraint on growth, Strategy & Research for the audit that makes the next phase honest.
Related: Why AI UI looks the same and Fix AI-built UI.
Book a call if you want a straight read on what to fix first.
What we will not claim
We will not tell you that AI tools are bad. They are not. They are scaffolding, not systems. We will not tell you to rewrite everything from scratch. Most teams need cleanup and rules, not a greenfield rebuild. We will not fix your product in a note. This is the path. The work is the work.