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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →The most reliable way to use an AI coding assistant for a bug is to make it investigate before it edits: preserve the exact failure, provide the smallest useful context, ask for a cause and supporting evidence, then request a narrowly scoped change and verify it yourself. An assistant can accelerate hypothesis-building, code search, reproduction and testing, but its explanation and patch still require human review.
Start with the observed behavior, not “fix my code”
AI tools work from the evidence you give them. A vague request invites guesses; a precise failure gives the assistant something it can test against the code.
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When the program exits with an error
Copy the complete error text, including exception type, message, stack trace and any file-and-line references. State the action that triggers it and what you expected to happen. Include relevant versions, input data and recent changes when they affect the failure.
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When I submit the checkout form, the request fails with this exact error:
TypeError: cannot read properties of undefined (reading 'id')
at createOrder (src/orders.js:48:27)
Expected: an order is created and the confirmation page opens.
Actual: the request returns HTTP 500.
The failure occurs only when a saved address is selected.Do not trim the stack trace to the line that looks suspicious. Frames above and below that line often show which input or call path produced the bad value.
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When the program runs but produces the wrong result
Describe the expected output beside the actual output. GitHub’s beginner debugging guidance distinguishes an early exit from an incorrect result; the second case can have no exception at all. Give the smallest input that demonstrates the mismatch, the relevant output format and any business rule the code is meant to enforce.
For input [2, 4, 6], the function should return 4.0 (the arithmetic mean).
It returns 12. The function is called by reportAverage in src/report.py:19.
Why does the output differ from the expected result?Ask for an explanation before asking for a patch
Use a two-stage conversation. First ask the assistant to explain the behavior and identify evidence in the code. Only after you agree that the diagnosis fits should you ask for an edit.
A diagnosis prompt that keeps the scope clear
GitHub Docs gives this literal starting point:
Explain in depth why my code produces the following error and how I can fix it:Place the exact error after the colon, then add the expected behavior, trigger and relevant files. For a wrong result, replace “error” with a description of why the output is incorrect and include expected-versus-actual values.
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Inspect the call path from the failing line to its inputs. List the assumptions that must be true for this code to work. Identify the most likely cause, cite the specific lines and runtime values that support it, and propose one way to reproduce the failure. Do not edit files yet.Make the assistant show its evidence
Ask which lines, assumptions, inputs or runtime values support the explanation. If it names several possible causes, have it rank them and state what observation would distinguish them. This turns a plausible-sounding answer into a hypothesis you can check.
Supply a minimal reproduction when you can: a short test, a single request payload, a command and its output, or a reduced project. Include a stack trace and debugger state rather than paraphrasing them. A debugger-aware assistant can use call stacks, frames, variable names and values; pasted prose cannot provide that context.
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Use a hypothesis loop instead of accepting the first answer
- Ask for the leading hypothesis and the evidence for it.
- Ask what evidence would disprove it and what instrumented log, assertion or test would collect that evidence.
- Run that check yourself or ask the tool to add temporary instrumentation without changing production behavior.
- Return the result and ask the assistant to update the diagnosis.
This interactive investigation matters because conversational debugging systems can make assumptions or skip root-cause localization when context is incomplete. A Microsoft Research evaluation describes interactive investigation as an important part of the process.
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Context should be relevant, bounded and safe to share. Start with the implicated files and their callers, then add configuration or dependencies that affect the failing path. Avoid dumping an entire repository when a small slice explains the behavior; excessive unrelated text makes it harder to distinguish signal from noise.
Repository and terminal agents
A terminal agent such as Codex can inspect a local repository, edit files and run tools already installed in that environment. Tell it which command starts the application, which command runs the relevant test and which files are in scope. Ask it to state the commands it plans to run before making changes when the project has destructive scripts or external side effects.
Keep secrets out of prompts and logs. Replace tokens, passwords and customer data with representative values while preserving the shape that triggers the bug. Record the redaction so the assistant does not mistake a placeholder for a literal value.
IDE-integrated debugger assistants
An IDE assistant can receive context that is difficult to paste accurately. Microsoft’s Visual Studio documentation describes debugger-aware help using call stacks and variable values, plus a Debugger Agent flow that can reproduce an issue, instrument the application, isolate a cause and validate the result against live execution. The exact features depend on the IDE, language and enabled extensions.
When a debugger is attached, pause at the failing line and provide the current frame, locals and relevant watch expressions. Ask the assistant to distinguish values observed at runtime from values inferred from source code. That distinction prevents a source-level assumption from being presented as a fact.
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Prerequisites are example-specific
GitHub’s introductory example assumes a Python project in VS Code with GitHub Copilot, Python and the Python extension. Those are requirements for that tutorial, not universal requirements for AI-assisted debugging. Your own workflow may use another language, IDE, terminal agent or test runner.
Choose a tool by the evidence it can access
No single documented approach is established as the overall best choice. Select the one that can see the evidence and execute the checks your bug requires.
| Approach | Repository and file access | Runtime or debugger context | Reproduction and local tools | Review controls | Best fit |
|---|---|---|---|---|---|
| Terminal agent such as Codex | Can inspect a local repository and edit files in the working tree. | Usually receives pasted logs, traces and values unless you provide a debugger integration. | Can run tools already installed locally, including project tests and linters. | Can inspect the diff and use Git checkpoints before and after a task. | Projects where terminal commands, scripts and repository-wide search are central. |
| IDE-integrated debugger assistant such as Copilot in Visual Studio | Works inside the IDE’s project context. | Can use call stacks, frames and variable values; the documented Debugger Agent can work with live execution. | Can support reproduction and instrumentation in the debugger workflow. | Review the suggested code and validate behavior in the IDE and test suite. | Failures that depend on a paused process, live state or an existing Visual Studio workflow. |
Compare candidates on six practical questions: Can it read the relevant files? Can it see actual runtime state rather than only pasted text? Can it reproduce the failure? Can it run your tests and other local tools? Does it explain changes clearly enough for review? Does it fit the IDE and terminal workflow your team already uses?
Investigate the proposed cause before changing code
Trace inputs through the failing path
Ask the assistant to walk from the trigger to the failure: request or user action, entry point, transformations, external calls, branch conditions and final operation. Have it name the value at each boundary and the invariant the next function expects. This often exposes a type mismatch, an empty collection, a missing property or an unexpected error response.
Separate root causes from symptoms
A null check at the crash line may stop the exception while silently discarding a required record. Ask whether the value became invalid earlier and whether the proposed guard preserves the intended behavior. Request alternatives when a fix could either reject bad input, supply a valid default or correct the upstream producer.
Check environmental and dependency assumptions
Have the assistant inspect package versions, feature flags, environment variables, serialization formats and platform-specific branches that affect the path. Confirm these facts against your actual environment; source code alone cannot prove what a deployed service returns or which configuration is active.
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Turn the explanation into a regression test
Before editing production code, ask for a test that fails for the reported input and would pass after the proposed correction. The test should encode the expected behavior, not merely assert that an exception disappeared. If the bug involves timing, concurrency or an external service, document the deterministic substitute or harness used for the check.
Request the smallest focused change
Once the cause is supported by evidence, constrain the implementation request. State the files or functions in scope, behavior that must remain unchanged, compatibility requirements and tests to run.
Implement the smallest fix for the confirmed cause.
- Change only src/orders.js and its unit test.
- Preserve the existing API response shape and validation rules.
- Add a regression test for a saved address that is missing an id.
- Do not refactor unrelated code or update dependencies.
Before editing, describe the exact change and any behavior it could affect.“Smallest” does not mean “fewest characters.” A clear validation error may be safer than a one-line fallback that creates corrupt data. Ask the assistant to explain the trade-off and identify callers that could observe the change.
Review the diff, not just the summary
Inspect every changed line. Check that the patch matches the diagnosis, handles the demonstrated input, preserves error handling and does not alter unrelated formatting or generated files. In a terminal workflow, create a Git checkpoint before the task and another after the accepted change so you can compare or revert cleanly; Codex CLI guidance documents this checkpoint-oriented practice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reproduce and verify the result
Run the original failing action
Use the same command, request, input and environment that produced the bug. Confirm both that the original failure is gone and that the expected result now occurs. An assistant saying “fixed” is not verification.
Run focused and neighboring tests
Start with the new regression test and the tests covering the edited function. Then run the relevant suite, type checker, linter or build. Neighboring cases matter because a guard or changed branch can fix one input while breaking valid empty, boundary or error cases.
Check the final diff and behavior
- Read the final diff after formatting and generated-code steps.
- Confirm no secret, debug print, temporary instrumentation or unrelated file was committed.
- Compare response status, output shape, logs and side effects with the pre-bug contract.
- Record the command and test result so another developer can reproduce the verification.
GitHub’s guidance emphasizes rigorous testing and human oversight. Microsoft’s debugger workflow also treats runtime validation as part of debugging rather than an optional afterthought.
What to do when the first fix fails
Do not ask for another speculative patch immediately. Return the new evidence and keep the conversation tied to the original hypothesis.
The proposed change is applied, but the regression test still fails.
New output:
AssertionError: expected 4.0, got 6.0
The input is unchanged. The diff changed reportAverage in src/report.py:19-22.
Revisit the diagnosis, explain which assumption was wrong, and propose the next diagnostic check. Do not edit files yet.- State exactly what changed and what did not.
- Paste the new error or test output in full, including the command used.
- Ask which part of the earlier explanation the result disproves.
- Collect the next distinguishing observation, such as an intermediate value or a call-stack frame.
- Only then request a revised, bounded patch and repeat the verification cycle.
This approach prevents layers of mutually inconsistent workarounds and leaves an auditable chain from symptom to evidence to change.
Understand the limits of AI-assisted debugging
Assistants generate useful hypotheses, but they do not automatically know your requirements, production configuration or acceptable trade-offs. They can infer a likely cause from a stack trace and still miss a data contract, race condition, security boundary or failure outside the supplied context.
A Microsoft Research paper reports a within-subjects evaluation of the ROBIN conversational debugging system with 16 industry professionals. In that study, ROBIN showed a 2.5× improvement in bug localization and a 3.5× improvement in bug resolution compared with the prior Visual Studio AI-assisted debugging condition. Those are study-specific results for that system and comparison; they are not a performance promise for every current coding assistant, project or developer.
The same paper mentions earlier work suggesting developers may spend more than 35% of development time debugging. That figure is a secondary citation in the paper, not an independently verified 2024 Microsoft Research measurement, so it should not be treated as a universal current baseline.
Keep the human responsible for deciding whether the explanation fits the observed behavior, whether the patch preserves functional and non-functional requirements, and whether the test evidence is sufficient. For security-sensitive, financial, medical or safety-critical code, apply your normal review, approval and release controls in addition to AI assistance.
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A reusable end-to-end checklist
- Capture the exact error or an explicit expected-versus-actual output pair.
- Record the trigger, input, file and line, runtime, versions and recent relevant changes.
- Ask for a diagnosis and supporting code or runtime evidence before requesting edits.
- Provide a minimal reproduction, stack trace, debugger state or focused repository context.
- Ask what observation would disprove the leading hypothesis.
- Convert the confirmed behavior into a regression test where practical.
- Request the smallest change, with files, invariants and commands in scope.
- Inspect the complete diff and create a reversible Git checkpoint.
- Rerun the original failure, the regression test and relevant neighboring checks.
- If it fails, supply the new evidence and revisit the diagnosis instead of stacking patches.
Prompt patterns you can adapt
Explain an exception
Explain in depth why my code produces the following error and how I can fix it:
[full error and stack trace]
Trigger: [command or user action]
Expected: [behavior]
Actual: [behavior]
Relevant files: [paths]
Do not edit files. Identify the likely cause, cite supporting lines or runtime values, and give one reproduction check.Explain incorrect output
The program runs, but the output is incorrect.
Input: [smallest reproducing input]
Expected: [exact output and rule]
Actual: [exact output]
Explain the mismatch, trace the relevant values, and propose a regression test. Do not change code yet.Implement and verify a bounded fix
Based on the confirmed cause, implement the smallest fix.
Scope: [files/functions]
Must preserve: [API, compatibility, validation, performance or security requirements]
Add or update tests for: [cases]
First describe the diff and possible behavior changes. Then edit, run [commands], and report the results. Do not modify unrelated files.These prompts work because they give the assistant an observable target, demand an explanation before generation and define how success will be checked.
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