Making AI-Generated Code Fail Gracefully

The article shows how an app that runs LLM-generated code can improve UX by retrying failures and feeding errors back to the model for self-correction.

Most mistakes are fixed within about three attempts, turning technical failures into clear, conversational guidance instead of raw Python tracebacks.

The author argues that apps executing AI-written code should hide internal errors from users, use them to repair outputs, and fall back to actionable messages when retries fail.

A debugging example also highlights a Qt pitfall: naming a custom QThread signal finished collided with Qt’s built-in signal.