AI models can analyze stack traces, spot pattern mismatches, and suggest fixes across languages in seconds. They are especially powerful for unfamiliar codebases, cryptic error messages, and subtle logic bugs that a human might spend hours tracking down. The key is giving the model enough context to reason about your specific problem.

Use this structure for maximum debugging accuracy:

I have a bug in my [LANGUAGE] code. Here is the context:

**What I expect to happen:**
[DESCRIBE EXPECTED BEHAVIOR]

**What actually happens:**
[DESCRIBE ACTUAL BEHAVIOR, PASTE ERROR MESSAGE/STACK TRACE]

**Relevant code:**
[PASTE THE FUNCTION OR MODULE — include imports and type definitions]

**What I have already tried:**
[LIST DEBUGGING STEPS TAKEN]

Please:
1. Identify the root cause of the bug
2. Explain why it happens
3. Provide a corrected version of the code
4. Suggest how to prevent this class of bug in the future

Claude leads for debugging thanks to its large context window (handles entire files) and precise reasoning about code flow. ChatGPT is strong for common patterns and quick fixes. DeepSeek excels at algorithmic and logic bugs. Gemini is good when you need to reference documentation. For complex multi-file bugs, Claude's 200K context is unmatched.

Do not paste only the error message without the code — the model needs both. Avoid sending your entire codebase; isolate the relevant function and its dependencies. Do not skip describing what you expected vs. what happened — "it does not work" gives the AI nothing to reason about. Never blindly apply a fix without understanding the explanation.

For intermittent bugs, ask: "What race conditions or timing issues could cause this behavior?" For performance bugs, include profiling output and ask: "Identify the bottleneck and suggest an O(n) alternative." Chain prompts: first ask for diagnosis, then ask for the fix, then ask for a unit test that would have caught the bug.