What AI is great at (and where it stops)
The parts of debugging AI reads well, the state it can never see, and why that gap becomes a confident wrong root cause.
AI is a fast reading partner for the parts of debugging that are pure pattern recognition. Hand it a traceback and it will name the likely exception type, point at the frame that matters, and explain what an unfamiliar error message means in plain language. It is excellent at spotting the common causes: an off-by-one loop, a value that was never checked for null, a type mismatch, a config key that does not exist. For a first read of a scary error, it saves real time.
The limits are just as important. The model cannot see your running system. It does not know the state of your database, the value of a variable at 3am, the depth of your queue, or the config that actually shipped. So it fills the gap with a plausible story, and a plausible story is often a confidently wrong root cause. It will announce that the bug lives in the retry logic when the real problem is a stale cache it never heard about.
The mental model that keeps you safe: treat the model as a fast hypothesis generator, not an oracle. It proposes; you dispose. Every suggestion is a lead to check against real evidence, not a verdict to act on. The rest of this course is built on that one distinction.
Take a recent bug you already solved. Paste only the error into an AI tool and read its guess. Note how close it got, and list what it could not possibly have known.
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