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AI for Debugging

5 min read
QaTest AutoSre

Sre

Paste metrics, logs, and error rates. Ask 'what's the most likely cause?' Then verify against your systems.

Qa

AI can help triage: 'Given these repro steps and this stack trace, what component is failing?' You validate.

AI for Debugging

TL;DR

  • AI is excellent at parsing stack traces, logs, and error messages. Paste, ask, get hypotheses.
  • Always verify. AI will confidently point at the wrong thing sometimes.
  • Best workflow: you gather context → AI suggests causes → you test and confirm.

Debugging is pattern-matching. AI is good at patterns. The marriage is obvious — but the divorce happens when you trust AI's answer without checking.

The Stack Trace Workflow

  1. Paste the full trace. Not a summary. Include the full stack, framework, and language.
  2. Add context. "We're on Node 20, Express 4. This happens when we pass a null user ID."
  3. Ask explicitly. "What's the root cause? What line or condition is wrong? Give me the fix."
  4. Verify. Run the fix. Or trace through the code. Don't ship based on "AI said so."

Common gotcha: AI will sometimes fix the symptom, not the cause. "Add a null check" might work, but the real bug is "why was user ID null upstream?" Push back. Ask "why would this happen?"

Log Analysis

Paste logs. Ask:

  • "What pattern do you see? Any errors or anomalies?"
  • "Which log lines are related to this request ID?"
  • "Summarize the sequence of events."

AI can help you sift through noise. It can't access your systems. You still need to correlate with metrics, traces, and real users.

Root Cause Hypotheses

When something's broken and you're stuck:

  • "Here's the symptom. Here's what we've ruled out. What are the top 3 most likely causes?"
  • "Given this architecture [paste], where could a race condition happen?"
  • "This worked before. We changed X, Y, Z. Which change is most likely to cause [symptom]?"

Use AI to generate hypotheses. You test them.

When AI Helps Less

  • Intermittent bugs — AI needs reproducible patterns. "It happens sometimes" is hard.
  • Environment-specific — "Works locally, fails in prod" — AI doesn't know your infra.
  • Proprietary or internal — AI hasn't seen your codebase. Paste the relevant parts.

Tool Choices

  • IDE (Cursor, Copilot): Good when you're in the file. Inline "explain this error" and "fix this."
  • Claude / ChatGPT: Good for long stack traces, log dumps, and "here's everything I know, what do you think?"
  • Custom agents: Some teams built internal tools that pipe errors to AI and suggest fixes. High leverage if you have volume.

You get a NullPointerException in prod. You stare at the stack trace. You grep the codebase. You add logging. Redeploy. Wait. 2 hours to find the cause.

Click "AI hypothesis → you verify" to see the difference →

## Debug Request Template

**Stack trace:** [paste full trace, not summary]

**Relevant code:** [snippet around the error]

**Context:**
- Runtime: [Node 20, Python 3.11, etc.]
- Framework: [Express 4, React 18, etc.]
- When it happens: [e.g., "when we pass null user ID", "under load", "first request only"]

**Ask:** What's the root cause? What line or condition is wrong? Give me the fix. Show corrected code.

**Verify:** Run the fix. Don't ship on "AI said so."

Quick Check

AI suggests 'Add a null check' for your NullPointerException. What should you do before shipping?

Do This Next

  1. Next time you hit a confusing error, paste the full stack trace into Claude or ChatGPT. Get a hypothesis. Verify before applying.
  2. Save a "debugging prompt" template — stack trace + code snippet + context. Reuse it.