GitHub Copilot Prompt for Debugging Code

Debugging is one of the most time-consuming tasks in software development. GitHub Copilot, integrated directly into your code editor, can significantly accelerate this process by analyzing your code, identifying likely causes of a bug, and suggesting targeted fixes. Unlike a manual search on Stack Overflow or in documentation, Copilot has access to the full context of your file and project, allowing it to provide much more relevant diagnostics. Whether you're facing a runtime error, unexpected behavior, or a performance issue, a well-structured prompt enables Copilot to act as an experienced pair programmer examining your code with fresh eyes. The goal is not just to fix a line but to understand the root cause of the problem to prevent it from recurring. In this guide, you will find optimized prompts to fully leverage GitHub Copilot's debugging capabilities, tailored to different expertise levels and types of bugs encountered daily.

The prompt

GitHub Copilot

Analyze the following code and identify all potential bugs. For each bug found:

Precisely describe the problem (relevant line, current vs expected behavior)
Explain the technical root cause
Propose a fix with corrected code
Indicate if this bug could cause other cascading issues

Context: [briefly describe what the code is supposed to do]
Observed error: [paste error message or describe unexpected behavior]
Environment: [language, framework, version]

[paste your code here]

After the analysis, suggest unit tests to verify the fixes work and prevent regressions.

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Answer 3 questions and Léa tailors the prompt to your situation.

Why it works

This prompt works because it structures the request by providing Copilot with the necessary context (code intent, observed error, environment) for an accurate diagnosis. Breaking it into numbered steps forces a methodical analysis rather than a superficial fix. Requesting tests at the end ensures a comprehensive approach beyond a simple patch.

Expected result

Copilot produces a structured diagnosis identifying each bug with its exact location, a clear explanation of the root cause, and corrected code ready to integrate. You also get targeted unit test suggestions to validate the fixes and prevent future regressions.

Variants by level

FAQ

Can GitHub Copilot debug code in any programming language?

GitHub Copilot supports the majority of popular languages (Python, JavaScript, TypeScript, Java, C#, Go, Rust, PHP, Ruby, etc.) with varying effectiveness. It performs particularly well on languages well represented in its training data like Python and JavaScript. For less common languages, always specify the language and its version in your prompt for more reliable results.

Copilot Chat or inline suggestions: which method is most effective for debugging?

For debugging, Copilot Chat (side panel or inline with Ctrl+I) is significantly more effective than automatic inline suggestions. Chat allows you to describe the problem in natural language, provide context, and iterate on the diagnosis. Use the /fix command in chat to directly request a fix, or select the problematic code before asking your question so Copilot focuses on the right portion of code.

How to improve the accuracy of Copilot's diagnostics when the bug is complex?

Three techniques significantly improve accuracy: first, always include the exact error message and full stack trace. Second, open related files in your editor as Copilot uses open tabs as additional context. Third, describe the steps to reproduce the bug and what you have already tried. If the initial diagnosis is incorrect, rephrase by eliminating false leads rather than repeating the same question.

How to use this prompt

Copy the prompt with the button above.
Paste it into ChatGPT, Claude or your favorite AI assistant.
Replace the bracketed variables with your details, then refine the result.

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