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Chapter 12

Debugging With AI

Every program you build will break. That is not a sign you are doing it wrong — it is the normal state of software. Bugs are not a failure of skill; they are what happens when real code meets real data, real users, and real cases nobody imagined. The difference between a frustrating afternoon and a five-minute fix is method. When you debug by vibe coding, you are not the one staring at the stack trace until it makes sense — the stack trace being the error report your program prints when it crashes, which we'll unpack later. You are the director: you reproduce the problem, gather the right evidence, and hand the AI a tight, well-framed case so it can do the heavy lifting. This chapter gives you a repeatable process for exactly that — one that works whether the bug is a one-line typo or a tangled race condition three layers deep. (A race condition is a timing bug: two things happen in an order you didn't expect, like two people grabbing the last seat at once.)

  1. 12.1The core mindset→
  2. 12.2A debugging checklist→
  3. 12.3Feeding the AI the right context→
  4. 12.4Reading errors you don't understand→
  5. 12.5Hypotheses, logging, and binary search→
  6. 12.6Root cause versus symptom→
  7. 12.7When the AI goes in circles→
  8. 12.8Understanding the fix before you keep it→
  9. 12.9The takeaway→
  10. 12.10Recap and Practice→

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