Error Taxonomy for AI UI Features
Explore how to classify different failure types in AI-powered user interfaces to improve reliability and troubleshooting. This lesson teaches you to identify observable signals behind errors, assign clear taxonomy labels for consistent UI states, and create actionable user outcomes. Learn to implement a stable error envelope for each chat turn that coordinates UI and logging, ensuring failures remain diagnosable and user actions are clear. By the end, you'll understand how to avoid generic error states and provide precise, reliable feedback in AI-driven chat applications.
Resumability makes sure a tool never runs twice by accident. This lesson is about the other half of reliability: giving every kind of failure a name specific enough that the UI and the logs can actually act on it. A user submits a prompt in the shipped chat UI, sees a spinner, and then the assistant panel goes blank. The Stop button is disabled, there is no retry option, and the UI does not indicate whether the request is still running, has failed, or has been canceled.
That ambiguity is not a bad model output. ...