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AI DEBUGGING

Debug Code With AI

Turn an error, failing test, or unexpected behavior into a structured debugging conversation that stays connected to the relevant project code.

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  • Project-aware workflow
  • Reviewable changes
  • Built for developers
Illustrative AI debugging screen showing an error trace and a proposed fix across project files.
Illustrative AICodeIDE product preview.

A PRACTICAL AI WORKFLOW

Work with AI without losing sight of the code

Good AI coding experiences connect natural-language intent with the actual software being changed.

01

Capture the failure clearly

Include the observed behavior, expected behavior, error output, and reproduction steps when available.

ErrorExpected resultReproduction
02

Connect symptoms to code

Relevant files, dependencies, and call paths can help an AI assistant form better debugging hypotheses.

Stack traceRelated filesDependencies
03

Test the hypothesis

A proposed fix is only a hypothesis until it is validated. Run the relevant tests or reproduce the original problem after the change.

FixTestsReproduction

ILLUSTRATIVE WORKFLOW

From a request to a reviewed change

The interface shown here uses dummy data for demonstration. A real development task can move through planning, project inspection, code changes, review, and validation.

  1. 01Describe the task and expected behavior.
  2. 02Identify the project context that matters.
  3. 03Generate or propose the required changes.
  4. 04Review the diff and validate the result.
Debug Code With AI workflow preview showing code changes and validation.
Illustrative AICodeIDE product preview.

EXPLORE MORE

Continue to the pages that cover adjacent coding workflows and developer use cases.

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FAQ

Frequently asked questions

Can AI debug runtime errors?

AI can analyze error information and code context to suggest likely causes and fixes, but the result must be verified in the actual runtime.

Can it debug failing tests?

Yes. Failing test output and the relevant test and implementation files can provide useful debugging context.

What if the AI's first hypothesis is wrong?

Iterative debugging is normal. Provide new evidence and refine the hypothesis rather than assuming the first suggestion is correct.

START BUILDING

Bring your next coding task into an AI-first workspace.

Start with a question, an existing project, or a new idea and move toward a reviewed implementation.

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