Flows category icon
Debugging
Intermediate

Fix App That Looks Good But Does Not Work

The systematic recovery flow for beautiful-but-broken AI builds: audit honestly, then fix by functional priority.

6 steps18 verify checks60-120 minutesWorks with: emergent · chatgpt · claude · cursor
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The Route

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Context Pack

Paste this first. It briefs the AI on requirements, constraints, and the Definition of Done before your first build prompt.

Context Pack
PROJECT CONTEXT:
My AI builder produced a polished-looking app where most functionality is fake or broken underneath.

GOAL:
Audit every feature honestly, then systematically convert fake functionality into working functionality.

REQUIREMENTS:
- A truthful feature-by-feature audit (works/fake/broken)
- Prioritized fix order by user impact
- Real implementations replacing each fake
- Persistence and states added where missing
- A final verified pass of the whole app

CONSTRAINTS:
- No new features until existing ones actually work
- Visual polish is not on the menu until function is fixed

DEFINITION OF DONE:
- Every advertised feature actually works end to end
- Data persists across refresh
- All async actions have loading and error states
- The final audit shows zero FAKE or BROKEN entries

COMMON FAILURES TO AVOID:
- Assuming a feature works because its UI renders
- Fixing cosmetics while core actions stay dead
- Claiming success without refresh/persistence tests
- Half-wired forms that validate but never submit

Paste this into your AI builder first. It teaches the AI what you want before you give it the build prompt.

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01Inventory and test every form
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01Inventory unpersisted state
02Design models and endpoints
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Best foryour first pass at this - no prior setup assumed

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