Debugging

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Debugging in AI is the work of finding out why a system behaves unexpectedly and fixing the real cause. This is not only about code errors. AI systems can fail because the data is wrong or the model learns a shortcut that produces strange predictions. For example, a vision model might perform well in testing but fail in production because the camera lighting changed, even though the code never changed.

To debug effectively, teams try to narrow down where the issue starts and how it affects the rest of the system. This can take time because AI systems rely on many connected pieces, and the source of a problem isn’t always obvious. Debugging is important because it helps ensure that models behave reliably and that their results can be trusted.

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