Field guides for React apps built with AI
Move from an impressive first preview to code you can inspect, test, recover, export, and hand to another developer with confidence.
Why AI app builders burn credits
Measure the cost of reaching an accepted, saved, locally runnable result—not the price of one prompt. Separate builder usage from hosting and runtime AI, record failed attempts, and require a ledger that explains estimates, actual charges, and refunds.
Read the guideHow to evaluate AI-generated React code
Require a coherent file graph, resolved imports, meaningful TypeScript, accessible controls, responsive behavior, predictable state, explicit environment boundaries, reversible edits, and a clean local production build.
Read the guideScreenshot-to-React is table stakes
Measure screenshot tools across seven dimensions: visual fidelity, responsive inference, semantic structure, interaction completeness, edit containment, recovery, and export readiness. A single desktop screenshot score misses most of the risk.
Read the guideHow to export an AI-generated React app
Freeze a known-good version, export source plus configuration, inspect the manifest, install in a clean directory, run type and production checks, test routes, document environment values, and commit the verified artifact before further work.
Read the guideFrom screenshot to production React
Extract the design system, define responsive transformations and states, map components and data, generate against a clear contract, verify one viewport at a time, test narrow edits, and finish with a clean export build.
Read the guide01
Current sources
Competitor facts link to official product documentation and carry a visible review date.
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Explicit methodology
Recommendations explain the acceptance criteria and evidence behind the conclusion.
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No fake certainty
Fast-changing plans and capabilities are framed as a dated snapshot, not permanent truth.