AI-Powered Debugging and Error Resolution

The iterative dialogue: Refining the user experience

Great applications are not born from a single prompt. They are shaped through iteration. In Cursor, this iteration includes debugging and error resolution, especially when features that function locally fail during promotion from development to staging to production environments. This lesson demonstrates how a conversational loop can be used with Cursor to reproduce failures, share exact logs, request minimal diffs, run the code again in the target environment, and harden fixes with tests. Along the way, we will address common promotion blockers such as dependency drift, environment or config mismatches, version or runtime changes, schema migrations, and build-pipeline quirks.

Step 1: First run and an AI-assisted debugging

In the previous lesson, we worked on establishing an initial UI foundation. The immediate next step is to see our work in action. Instead of manually creating a virtual environment, installing dependencies, and running terminal commands, we can simply state our intent and let the Cursor agent manage the entire process. This is a core workflow in an AI-first editor.

We give the AI a clear, direct command in the chat panel:

Cursor’s agent analyzes this request. It recognizes that this is a Flask project, identifies the missing code pieces to run the app, creates the necessary files, and adds the missing pieces of code to run the app.

No development process is without friction. In fact, our first attempt to run the freshly scaffolded application results in an error.

This is not a failure, but our first opportunity to see how AI-assisted debugging transforms troubleshooting. Instead of deciphering a cryptic stack trace and searching Stack Overflow, we simply engage the AI.

We also notice duplication of project folders, as we can see below:

We provide Cursor with the necessary context, the screenshot of the error and the Terminal output as context in the chat. We also share our concern around duplicate folders.

The AI immediately understands the context of the error, diagnoses the root cause, and proposes the fix: