How to Debug with AI: Turn Errors into Solutions

Why use AI to debug?

Debugging is a large part of software development—and it’s also one of the best places to let AI speed up your thinking. As we covered in our previous guide on prompting (Episode 3), clear, structured prompts get better results. In this episode of Vibe Coding 101 you’ll learn a repeatable workflow to turn error messages and failing tests into solid fixes using AI-powered assistants.

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The three rules before you paste anything

Before you paste an error into an AI chat, follow these quick sanity checks so you both save time and protect sensitive info:

• Sanitize secrets: Remove API keys, passwords, session tokens, and proprietary file paths. Replace with placeholders like .
• Minimal but complete: Include the smallest code snippet or command that reproduces the issue and the full error/stack trace. Don’t paste entire projects unless requested.
• Environment context: Tell the AI your OS, language/runtime (and version), package manager, and any relevant frameworks (e.g., "Node 18.16.0, npm 10.2.0, Express 4.18").

These practices reduce noise and help the model focus on the real cause.

How to structure the prompt

A high-quality debugging prompt has four parts. Use this template every time:

1. What you expected — One sentence about intended behavior.
2. What happened — The exact error message and a one-line summary of symptoms.
3. Context — Minimal reproducible code, environment, and versions.
4. Request — Specific asks (e.g., "Explain root cause", "Suggest two fixes", "Provide a patched code snippet and a unit test").

Example prompt:

"I expected my Express endpoint to return JSON { success: true } when POST /login is called. Instead I get a 500 with 'TypeError: req.body is undefined'. I'm using Node 18.16.0 and Express 4.18, body-parser is installed. Here is the minimal route code: [paste snippet]. Explain the root cause, propose two fixes (one minimal change and one best-practice), and provide a unit test example."

This clarity lets the AI produce targeted, testable fixes instead of vague suggestions.

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Iterative debugging workflow — turn suggestions into verified fixes

Use this loop every time you debug with AI. Treat the model like a collaborator, not an oracle.

1. Ask for hypotheses: Have the AI list 2–4 plausible causes and explain why each could produce the observed error.
2. Pick a hypothesis to test: Prefer the simplest change that could prove or disprove the theory.
3. Implement the test: Make the change in a branch or local environment, run a reproducible test (unit test or manual script), and capture the new output.
4. Report back: Paste the new logs and ask the AI to update its diagnosis.
5. Repeat until fixed.

Why this works: AI excels at generating plausible root causes, but you still need automated runs to validate them. Iteration refines hypotheses and prevents overfitting to one suggestion.

Example: From error to patch

Walkthrough (shortened):

• Symptom: "Unhandled exception: Cannot read property 'map' of undefined" when rendering user roles.
• Prompt to AI: provide component code, sample data shape, versions (React 18, TypeScript 5). Ask: "Why is this happening and how to fix safely?".
• AI hypotheses: data is undefined, property is misspelled, async fetch returns null.
• Test: add a console.log for the data before map; implement optional chaining (data?.map) as a minimal fix and a guard clause as the best-practice fix.
• Outcome: minimal fix prevented the crash; best-practice fix added clearer error handling and a unit test.

This example shows both a quick stop-gap and a long-term improvement—both valuable.

Prompt patterns that get consistent results

Try these short templates depending on your goal:

• Quick fix: "Error: [paste] — minimal code: [paste]. Suggest one-liner fix and explain why it works."
• Root cause analysis: "Given this stack trace [paste], list 3 likely root causes and how to test each."
• Full example patch: "Fix this function with a correct implementation, add one unit test (Jest) and explain test intent."

Use numbered requests when you want multiple distinct outputs (e.g., "1) short explanation 2) two fixes 3) unit test").

Safety, IP, and reproducibility best practices

• Never paste secrets: use placeholders for anything sensitive.
• Create a reproducible repo or Gist: if the bug is complex, provide a minimal repo link rather than massive dumps. Use private repos if code is proprietary and only share with trusted tools.
• Pin versions: include package versions to prevent environment mismatch (e.g., Python 3.11.2, Django 4.2.3).
• Record iterations: keep a short log of AI suggestions and test results in your issue tracker. It helps future debugging and accountability.

Tools worth integrating (Feb 27, 2026 examples)

Here are widely used AI and developer tools you can plug into a debugging flow. Check vendor pages for the latest specifics.

• OpenAI / ChatGPT — conversational assistant for debugging and explanation (consumer plans historically included ChatGPT Plus around $20/mo for priority access; see OpenAI for up-to-date pricing and model names).
• GitHub Copilot — inline code suggestions and tests (Copilot Individual and Copilot for Business have paid tiers; Copilot also integrates with GitHub Issues for context-aware suggestions).
• Replit Ghostwriter — in-editor AI coding and quick run environment for minimal reproducible examples.
• Snyk/Dependabot — static dependency scanning and fix PRs for security-related failures.
• VS Code + AI extensions — many IDEs now offer AI assistants that can generate quick fixes; keep your editor updated and pin extension versions.

Note: pricing and plan names evolve—always verify on vendor sites. Use free tiers to experiment before committing to paid plans.

When AI gets it wrong (and how to recover)

AI suggestions can be incorrect or risky. When that happens:

• Re-check assumptions: did you provide the right stack trace, file, and version info?
• Ask for an explanation: if the model suggests a change, request a succinct reasoning line ("one-line rationale").
• Request alternatives: ask the AI for 2–3 different solutions and the trade-offs.
• Fallback to instrumentation: add logs, breakpoints, and tests to measure real behavior instead of trusting the suggestion blindly.

Treat AI outputs as educated guesses that become reliable only after you validate them.

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Practical checklist to keep nearby

• Paste the full error/stack trace, not just the last line.
• Include minimal reproducible code + sample input.
• State expected vs. actual behavior in one sentence each.
• Provide environment and version context.
• Ask for tests and a one-line rationale for each change.

Wrap-up — make AI part of your debugging muscle

Debugging with AI is a skill: it’s about asking the right questions, isolating failures, and validating fixes. As we mentioned in Episode 2 (building your first app), speed comes from repeatable practices—this episode gives you that practice for debugging. Use clear prompts, iterate quickly, and always validate changes with automated tests or reproducible runs. Over time, you’ll find AI moves you from guesswork to predictable, verifiable fixes.

If you liked this episode, don’t miss Episode 5 where we’ll cover integrating AI-driven tests into your CI pipeline to catch regressions earlier.

Happy debugging—turn those red error bars into green tests.

Frequently Asked Questions

What should I include when pasting an error to an AI?

Include the full error/stack trace, a minimal reproducible code snippet, environment details (OS, language/runtime versions), and a one-sentence expected vs. actual behavior.

Can I share private code with AI tools?

You should avoid sharing secrets and proprietary data with public AI tools. Use private or enterprise tools and sanitize sensitive content before sharing.

How do I know AI's suggested fix is safe?

Validate every AI suggestion with tests or reproducible runs. Ask the AI for a one-line rationale and multiple alternatives, then pick the one you can verify.

Which AI tool is best for debugging?

There’s no single best tool — ChatGPT/OpenAI assistants, GitHub Copilot, and IDE-integrated AI each help different parts of debugging. Use them together: conversation for diagnosis, Copilot for inline suggestions, and local runs for verification.