Tutorial 7: Use AI for Debugging

AI is great at "what does this error mean?" Not always great at "why does this happen?"

Step 1: Paste the Stack Trace (5 min)

"Getting this error. What's likely the cause?

[paste full stack trace]"

Often AI:

• Translates the error

• Names common causes

• Points to a likely fix

For unfamiliar errors: faster than Googling.

Step 2: Include the Code (10 min)

"This stack trace plus the function it points to:

[stack]

```python
def my_func():
    ...

What's the bug?"

Context. AI can spot the issue with both.

Without code: AI guesses from the error text alone.

## Step 3: State What You've Tried (10 min)

"I'm seeing X. I've tried:

• Restarting the service

• Checking env vars

• Verifying the DB is reachable

The error persists. Any other ideas?"

Saves AI suggesting the obvious. Gets to less-common causes faster.

## Step 4: Ask for the Mental Model (10 min)

"I don't understand why this throws. Explain how Promise.all handles rejection."

AI is good at explaining concepts. Often: the explanation reveals your bug.

Better than "fix this" — you learn the why.

## Step 5: Have AI Generate Hypotheses (10 min)

"This bug is intermittent. About 1 in 100 requests fails with X. Give me 5 plausible causes."

AI brainstorms:

- Race condition
- Timeout
- Connection pool exhaustion
- Concurrency on shared state
- ...

Now you have a list to investigate.

## Step 6: Don't Trust the Diagnosis (15 min)

AI is confident. AI is sometimes wrong:

- Hallucinated cause
- Outdated docs
- Plausible-sounding but irrelevant

Verify each suggestion:

- Check the actual code path
- Read the actual docs
- Reproduce in isolation

If AI says "this could be a race condition," look at the actual concurrency. Don't just believe.

## Step 7: Use AI for Repro Steps (10 min)

"I see X in production. Help me write a minimal reproduction."

AI proposes a smaller version. Often: trying to reproduce surfaces the cause.

Reduction is a skill AI accelerates.

## Step 8: Test Your Hypothesis (10 min)

After AI suggests cause:

- Write a test that proves it
- Fix it
- Verify the test passes

Without verification: you might fix something else and think you fixed this.

## Step 9: Save the Solution (5 min)

After solving:

- Add a regression test
- Comment the fix if non-obvious
- If the bug class is common: document for the team

The next person hitting this bug: don't make them re-AI-debug.

## Step 10: When to Stop and Think (10 min)

If AI's suggestions aren't landing after 30 minutes:

- Step back
- Reproduce manually
- Use a debugger
- Read the code carefully

AI is a tool. When it's not helping, switch tools.

Don't spiral in AI-loop. Sometimes traditional debugging wins.

## What You Just Did

AI debugging: stack + code, what you've tried, mental models, hypotheses, verify, repro, test, save solution, step back when stuck. Faster bug hunts.

## Common Failure Modes

**Paste error; expect magic.** Without code/context: weak help.

**Believe AI's confident wrong answer.** Investigate the actual cause.

**No test for the fix.** Bug recurs.

**Loop with AI forever.** Stuck; should've used a debugger.

**Don't share findings.** Team re-debugs.