Debugging with AI - Use AI - ggcoder.com

Give the AI the Right Context

The quality of the AI's answer depends entirely on the quality of context you provide. When debugging, always include: the full error message and stack trace, the relevant file, steps to reproduce, and what you have already tried.

Bad vs Good Debug Prompts

Debug Prompt Quality

1# Bad — vague, no context2"my app is broken, fix it"34# Bad — partial error only5"I get TypeError, what's wrong?"67# Good — full context8"Getting this error when submitting the login form:910TypeError: Cannot read properties of undefined (reading 'token')11  at handleSubmit (src/components/LoginForm.tsx:42:28)12  at HTMLFormElement.<anonymous> (src/components/LoginForm.tsx:18:5)

Rubber-Duck Debugging with AI

Sometimes you don't need the AI to fix the bug immediately — you need it to help you think systematically. Ask the AI to form hypotheses about the root cause and rank them by likelihood before making any changes.

Hypothesis-First Debugging

1# Ask for hypotheses first2"Before fixing anything, here is the bug and the relevant code.3List the 3 most likely root causes, ranked by probability.4Explain your reasoning for each."56# Then narrow down7"Hypothesis #1 seems most plausible. What would you check8to confirm or rule it out without changing any code yet?"

This approach avoids time-wasting trial-and-error loops and helps you understand the problem rather than just applying random patches.

Reading Stack Traces and Cryptic Errors

AI is excellent at decoding complex error messages, especially from bundlers, compilers, or third-party libraries. Paste the entire stack trace and ask for a plain-English explanation of each part.

Stack Trace Analysis

1# Ask for plain-language explanation2"Explain this stack trace in plain English.3Which line is the actual source of the error vs4which are just propagation frames?56[paste full stack trace]"78# Diff expected vs actual behavior9"Here is the function and its test.10The test expects X but gets Y.11Walk me through the execution step by step12to find where behavior diverges."

Adding Targeted Logging and a Failing Test

Instead of guessing, let the AI suggest exactly which log statements to add to narrow the problem. Then ask it to write a failing test that reproduces the bug — when that test passes, the bug is fixed.

Logging and Reproducing Test

1# Ask for targeted logging2"Without changing any logic, add console.log statements3to this function that would tell us the value of4each variable at the point the error occurs."56# Ask for a reproducing test7"Write a failing unit test that reproduces this bug.8The test should pass once the bug is fixed.9Use the existing test patterns in src/__tests__/."1011# Then fix with the test as a safety net12"Now fix the bug. The test you just wrote must pass13and no existing tests should break."

Tip

A failing test written before the fix is the clearest evidence that you are solving the right problem.

Common Pitfalls When Debugging with AI

AI can confidently give wrong answers. Recognizing the warning signs early will save you significant time.

API Hallucination

AI sometimes invents method names, parameters, or config options that do not exist — especially for newer or less popular libraries. Always check official docs when the AI suggests an API you have not seen before.

Confident but Wrong Fixes

AI can apply a patch that looks reasonable but does not address the root cause — or worse, masks the symptom while the real problem remains. If an AI fix "resolves" the error but you do not understand why, ask for an explanation.

Warning

If you find yourself applying a third or fourth patch to the same bug, stop. Ask the AI for root-cause analysis instead of continuing to patch: "Stop patching. Explain the root cause of this bug and suggest an architecturally correct fix."

Summary

Always provide the full error, relevant file, reproduction steps, and what you already tried.
Ask the AI to form and rank hypotheses before making any changes.
Use AI to explain stack traces and identify which frame is the actual error source.
Ask the AI to add targeted logging or write a failing test that reproduces the bug before fixing it.
Check official docs when AI suggests unfamiliar APIs — hallucinated APIs are a common trap.
If you are applying a third patch, stop and request root-cause analysis instead.

1# Bad — vague, no context2"my app is broken, fix it"34# Bad — partial error only5"I get TypeError, what's wrong?"67# Good — full context8"Getting this error when submitting the login form:910TypeError: Cannot read properties of undefined (reading 'token')11 at handleSubmit (src/components/LoginForm.tsx:42:28)12 at HTMLFormElement.<anonymous> (src/components/LoginForm.tsx:18:5)

1# Ask for hypotheses first2"Before fixing anything, here is the bug and the relevant code.3List the 3 most likely root causes, ranked by probability.4Explain your reasoning for each."56# Then narrow down7"Hypothesis #1 seems most plausible. What would you check8to confirm or rule it out without changing any code yet?"

1# Ask for plain-language explanation2"Explain this stack trace in plain English.3Which line is the actual source of the error vs4which are just propagation frames?56[paste full stack trace]"78# Diff expected vs actual behavior9"Here is the function and its test.10The test expects X but gets Y.11Walk me through the execution step by step12to find where behavior diverges."

1# Ask for targeted logging2"Without changing any logic, add console.log statements3to this function that would tell us the value of4each variable at the point the error occurs."56# Ask for a reproducing test7"Write a failing unit test that reproduces this bug.8The test should pass once the bug is fixed.9Use the existing test patterns in src/__tests__/."1011# Then fix with the test as a safety net12"Now fix the bug. The test you just wrote must pass13and no existing tests should break."