Debugging Habit Reset

Classic debugging often starts with "where should I put the next log?" or "where should I set a breakpoint?" AI-assisted debugging starts with a different question: "Why would this symptom occur?"

The Old Loop

These tools remain useful. The problem is direction. They tell you what happened at a point; they do not rank where to look first.

Hypothesis-Driven Debugging

Debugging Context Template

### Symptom
- Exact error message
- Expected vs actual behavior

### Reproduction
- Environment
- Steps
- Frequency

### Already checked
- Facts confirmed
- Hypotheses ruled out

### Suspected area
- Recent changes
- Related modules
- What feels unusual

Bad request:

Fix this error:
TypeError: Cannot read property 'map' of undefined

Better request:

Symptom: User list page throws "Cannot read property 'map' of undefined" only on first load.
Reproduction: after login, direct navigation to /users. Sidebar navigation does not reproduce it.
Checked: users state initializes as [], API returns 200 with array data.
Suspected area: SSR/CSR hydration timing or useEffect ordering.
Relevant files: app/users/page.tsx, hooks/useUsers.ts.
Return likely causes ranked by probability and one verification step for each.

What AI Is Good At

• Ranking likely causes from error type and framework patterns.
• Noticing mismatches between server and client assumptions.
• Suggesting quick verification steps.
• Explaining why a symptom appears only under certain conditions.
• Producing a minimal fix after the cause is confirmed.

What Humans Still Own

• Reproducing the bug.
• Confirming facts instead of accepting plausible explanations.
• Choosing the safest fix for the system.
• Adding regression tests.
• Writing the postmortem or runbook when the bug matters.

Debugging Review Checklist

Next

Read Context Management to improve the quality of the information you give AI.