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Use an AI coding assistant as an investigator before using it as a code generator. Give it the exact failure, expected and actual behavior, relevant files, and runtime evidence; ask it to explain and support a likely cause; request the smallest safe change; then inspect the diff and rerun the failure and related tests yourself. This sequence turns an assistant from a guess-producing chatbot into a hypothesis-and-verification aid.
What AI-assisted debugging should accomplish
A useful debugging session has two separate outcomes: a defensible explanation of why the program behaves incorrectly and a change that restores the intended behavior without breaking neighboring cases. AI can help with both, but only when the conversation stays anchored to observable evidence.
Start by deciding which kind of bug you have:
- Failure with an error: the program exits, raises an exception, returns an error response, or fails a test. Preserve the complete error text, including the exception type, message, stack trace, file names, and line numbers.
- Incorrect behavior without an error: the program runs but produces a result that differs from the requirement. Record the exact input, expected output, actual output, and any relevant state.
GitHub’s beginner debugging guidance makes this distinction because the assistant needs different evidence for an exception than for a semantic mismatch. “Fix it” is not a diagnosis. A precise symptom lets the tool form a testable hypothesis.
A repeatable AI debugging workflow
| Phase | Your objective | What to give the assistant | What you must decide |
|---|---|---|---|
| 1. Describe | Define the observed failure | Exact error or expected-versus-actual result, trigger, environment | Whether the report is complete and reproducible |
| 2. Explain | Generate a causal hypothesis | Relevant code, stack trace, inputs, configuration, recent changes | Whether the explanation fits the evidence |
| 3. Investigate | Test the hypothesis | Requests for line-level reasoning, runtime values, and a minimal reproduction | Which observation would confirm or falsify it |
| 4. Change | Apply a bounded correction | Explicit scope, constraints, and acceptance criteria | Whether the proposed diff preserves requirements |
| 5. Verify | Demonstrate the behavior is corrected | Test commands, failing case, neighboring cases, and new output | Whether the evidence is sufficient to merge |
Do not skip from the first phase to code generation. If a proposed fix fails, return the new test output and what changed, then ask the assistant to revisit its diagnosis. Layering speculative patches makes the code harder to reason about and can hide the original defect.
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1. Describe the symptom precisely
For an exception or failed command
Copy the complete error rather than paraphrasing it. Include the action that triggers it, the command or request used, the relevant file and line, and the runtime or dependency version when it could affect behavior. Redact secrets, access tokens, private customer data, and proprietary code that the assistant does not need.
Trigger: POST /orders with an item whose quantity is 0
Expected: HTTP 400 with {"error":"quantity must be positive"}
Actual: HTTP 500
Error:
TypeError: 'NoneType' object is not subscriptable
at validate_order (orders/validation.py:42)
at create_order (orders/routes.py:18)
Environment: Python 3.12.2, service commit 8f31c2aKeep the stack trace intact. Its call order tells the assistant where the failure surfaced, while the trigger and expected response define what “fixed” means.
For incorrect output
Show a small, representative input and compare the two results explicitly. Include units, ordering rules, rounding rules, time zone, locale, and whether the result is deterministic. “The total is wrong” leaves too many possible causes; “expected 10.00, received 9.99 for two prices of 4.995 under banker’s rounding” narrows the search.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsInput: prices = [4.995, 4.995]
Expected total: 10.00 (round after summing, two decimal places)
Actual total: 9.98 (each item rounded before summing)
Relevant function: billing/calculate_total.py, lines 12–31State what changed
If the bug appeared after a commit, dependency upgrade, feature flag change, migration, or configuration edit, identify that change and provide the before-and-after behavior. A likely regression point is more useful than a broad request to inspect the entire repository.
2. Ask for an explanation before a patch
Use a prompt that requests causal reasoning and explicitly postpones editing. GitHub Docs gives this exact wording for an error-driven investigation:
Explain in depth why my code produces the following error and how I can fix it:Follow the colon with the complete error and the surrounding context. For a wrong result, adapt it:
Explain why this input produces the actual output instead of the expected output. Trace the relevant code paths, identify the most likely cause, state what evidence supports it, and list any assumptions. Do not change files yet.Ask the assistant to distinguish facts from assumptions. A good response should identify the execution path, the value or invariant that becomes incorrect, and the smallest location where the behavior diverges from the requirement. If it only restates the error or proposes a generic rewrite, ask for line-level evidence and a reproduction step.
3. Give the assistant enough context, but not everything
Start with the smallest useful slice
Provide the implicated function, its callers, relevant data types, configuration, and the test or request that reproduces the problem. Add neighboring definitions when a symbol’s behavior is not obvious. A focused context window makes it easier to notice the actual invariant and reduces unrelated suggestions.
- Repository layout or the paths of the relevant modules.
- Exact command, request, test name, or UI action used to reproduce the issue.
- Stack trace, logs, debugger frames, and selected variable values.
- Expected behavior from a specification, issue, or acceptance test.
- Recent diff, feature flag, schema change, or dependency version involved.
- Constraints such as public API compatibility, supported runtimes, performance limits, and migration policy.
Use a minimal reproduction when possible
A minimal reproduction removes unrelated services and data while preserving the failure. It gives the assistant a concrete experiment and gives you a fast way to test a proposed change. Record the command that runs it and the result before changing code.
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Protect sensitive information
Replace credentials, signing keys, personal data, production hostnames, and confidential business rules with stable placeholders. Tell the assistant which values were redacted and whether the substitution could affect the diagnosis. Never paste a production secret merely because a log line contains it.
4. Investigate the proposed cause
Before accepting a diagnosis, ask how it can be checked. Useful follow-up questions include:
- Which exact lines create the incorrect value or exception?
- What are the input, intermediate, and output values at that point?
- Which precondition or invariant is violated?
- What alternative causes remain plausible, and what observation would distinguish them?
- Can you propose a minimal reproduction or a failing test that demonstrates the cause?
- What behavior might change if this code is modified?
Do not treat confident language as evidence. Compare the explanation with the stack trace, source, logs, and requirements. If the assistant claims a null value, inspect where that value is assigned. If it blames a race condition, ask for an interleaving or timing observation that demonstrates one. If it points to a dependency, check the installed version and release behavior rather than assuming the claim is true.
5. Use debugger and runtime context when it is available
Pasted text is useful, but a debugger can expose the state that actually produced the failure. An IDE-integrated assistant can inspect call stacks, stack frames, variable names, and current values. Microsoft’s Visual Studio documentation also describes a Debugger Agent flow that can reproduce an issue, instrument an application, isolate a cause, and validate a change against live execution.
Capture a meaningful execution state
- Stop at the first relevant exception or at the point an invariant becomes false, not only at the final error handler.
- Record the call stack and the values of inputs, flags, object properties, and collection lengths.
- Note whether the run is a debug build, which configuration is active, and whether external services are mocked.
- Compare a failing run with a successful run when you can reproduce both.
Runtime context does not eliminate the need for reasoning. It is a snapshot from one execution; it may not reveal a data-dependent, timing-dependent, or deployment-only condition. Ask the assistant what additional run or log would test its hypothesis.
6. Ask for the smallest focused change
Once the cause is supported, define the patch’s boundaries. State the files or function that may change, behavior that must remain unchanged, and the tests that must pass. For example:
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Preserve the public route contract and existing valid-order behavior.
Add or update a regression test for quantity=0 and retain coverage for quantity=1 and a negative quantity.
Do not upgrade dependencies or reformat unrelated files.
Before editing, describe the intended change and any behavior it could affect.A bounded request prevents an assistant from turning a one-line defect into a broad refactor. If the assistant proposes several alternatives, compare them by correctness, compatibility, complexity, and testability. Choose one deliberately rather than allowing the tool to pick by default.
Ask for a regression test
The test should fail on the old code and pass on the new code. It should encode the requirement, not merely the current implementation. For a parser bug, include the malformed input and a valid control case. For an authorization bug, test both the denied and permitted roles. For a concurrency issue, a deterministic test may need a controlled scheduler or synchronization primitive rather than a sleep.
7. Review the diff and preserve a recovery point
Inspect every changed line yourself. OpenAI’s Codex CLI guidance describes a workflow in which a terminal agent inspects a local repository, edits files, runs locally installed tools, and creates Git checkpoints before and after a task. The checkpoint is valuable even when you use another assistant: it gives you a clean comparison and an immediate rollback path.
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- Read the diff, not only the assistant’s summary.
- Confirm that only the intended files changed.
- Look for altered error handling, logging, validation order, defaults, and public interfaces.
- Check that tests assert the required behavior rather than matching an implementation detail.
- Reject unrelated formatting, dependency, generated-file, or configuration changes unless you requested them.
Ask the assistant to explain each non-obvious line in the diff. If you cannot state why a line is necessary, do not merge it yet.
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8. Reproduce and verify the result
Run the original failure first
Repeat the exact command, request, test, or user action that failed. Confirm that the original error is gone or that the output now matches the stated expectation. A successful build alone does not prove a runtime bug is fixed.
Run focused and neighboring tests
Start with the regression test and the affected module, then run the broader suite appropriate to the project. Include boundary values, valid controls, malformed inputs, permission variants, and error paths adjacent to the defect. For a change involving dates, currency, encoding, or concurrency, add the edge cases that exercise those rules.
Check the final state
- Review the final diff and repository status.
- Confirm linting, type checking, builds, migrations, and security checks required by the project.
- Compare logs and metrics if the bug affects a service.
- Verify that the fix works in the supported environment, not only in an assistant-created local scenario.
GitHub’s debugging guidance and blog emphasize rigorous testing and human oversight. The assistant’s statement that a problem is fixed is not a verification result.
9. Continue with evidence when the first fix fails
If the test still fails, do not ask for “another fix” without context. Give the assistant the new output, the exact command, the diff that was applied, and what behavior changed. Ask it to revisit the original hypothesis and identify which assumption was wrong.
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Command: pytest tests/test_orders.py::test_zero_quantity
Expected: 400
Actual: 500
New traceback: [paste complete traceback]
Diff: [paste or point to the changed lines]
Re-evaluate the diagnosis. Explain what the new evidence rules out and propose the next smallest experiment before editing again.This loop separates diagnosis from repair. It also creates a record of rejected hypotheses, which helps another developer continue the investigation without repeating blind experiments.
Terminal agents and IDE debugger assistants
There is no evidence in the cited material that one category is universally best. Choose the environment that can see the evidence your bug requires.
| Capability | Terminal coding agent | IDE-integrated debugger assistant | Decision question |
|---|---|---|---|
| Repository and file access | Can inspect a local repository and edit files within the permitted workspace. | Can work from the project loaded in the IDE, subject to its workspace and permissions. | Where is the authoritative code and configuration? |
| Runtime state | Usually receives pasted logs, command output, or debugger data you provide. | Can use debugger context such as call stacks, frames, and variable values; documented flows may use live execution. | Does the diagnosis depend on values that are difficult to paste? |
| Reproduction | Can run locally installed commands and tests when the environment permits. | Can launch and debug the application through the IDE; some documented agent flows instrument and reproduce issues. | Which setup reproduces the failure most faithfully? |
| Change control | Supports explicit file scope, command output, and Git checkpoints. | Offers in-editor navigation and debugger-to-code context; review controls depend on the IDE workflow. | Where will you inspect and approve the diff? |
| Best fit | Repository-wide searches, scripts, tests, and command-line services. | Interactive stepping, visual state inspection, and application sessions already run in the IDE. | Which tool matches the team’s existing workflow? |
GitHub’s beginner example uses a Python project in Visual Studio Code with Copilot, Python, and the Python extension. Those are prerequisites for that example, not universal requirements for AI-assisted debugging.
Prompt patterns you can reuse
Error explanation
Explain in depth why my code produces the following error and how I can fix it:
[paste the complete error and stack trace]
Trigger: [command, request, or action]
Expected behavior: [specific result]
Environment: [runtime, dependency, or configuration versions]
Relevant files: [paths]
Do not edit files yet. Identify the execution path, the likely root cause, supporting evidence, alternative hypotheses, and a minimal test that would distinguish them.Incorrect output
Why does this code produce [actual output] instead of [expected output] for [input]?
Trace the values through the relevant functions, identify the first point where they diverge, and state the rule or invariant that is violated. Do not change code until I approve the diagnosis.Diff review
Review this proposed diff against the stated requirement. List behavior changes, edge cases, compatibility risks, and missing tests. Do not rewrite it. Mark each concern with the affected file and line.Verification plan
Given the bug report and proposed fix below, create a verification plan ordered from fastest to broadest. Include the original reproduction, a regression test, neighboring cases, required commands, and the result that would count as success. Identify any claim that cannot be verified locally.Common failure modes in AI-assisted debugging
Vague prompts produce generic repairs
“My app is broken; fix it” omits the symptom, trigger, and acceptance condition. Supply an exact error or an expected-versus-actual comparison.
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The assistant edits before locating the cause
A patch can make the visible error disappear while preserving the underlying defect. Require an explanation, evidence, and a reproduction before permitting edits.
Too much unrelated context hides the signal
Pasting an entire repository without identifying the failing path makes it harder to focus. Start with the smallest useful slice and add files when the assistant identifies a dependency.
One successful test is mistaken for proof
A single happy-path test may miss boundary values, authorization changes, race conditions, or error handling. Run the original failure, a regression test, neighboring cases, and the project’s required checks.
Confident hallucinations become “facts”
Assistants may invent APIs, misread versions, or assume an implementation detail. Ask for the source line, runtime value, command, or documentation that supports each material claim, then verify it independently.
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Keep a Git checkpoint and review each diff. If a patch fails, return the new evidence and revisit the hypothesis instead of stacking unrelated edits.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What published evidence says about effectiveness
A 2024 Microsoft Research study evaluated a conversational debugging system called ROBIN with 16 industry professionals. In that study, the authors reported 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 figures describe that tested system, participants, tasks, and comparison; they are not a guarantee for every current AI coding tool, project, or developer.
The same paper discusses 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.
The practical conclusion is narrower and more useful: interactive investigation can improve the information available to an assistant, while human oversight is still required to decide whether the cause is real, the change preserves requirements, and the tests are adequate. Microsoft’s documentation, GitHub’s guidance, and OpenAI’s Codex documentation all describe workflows that combine tool assistance with developer review rather than autonomous acceptance.
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A team checklist for safe AI debugging
- Write the trigger, exact error or output mismatch, expected result, and environment.
- Capture the smallest reproducible case and redact secrets and personal data.
- Ask for an explanation and supporting evidence before requesting edits.
- Use stack frames, variable values, logs, or instrumentation when runtime state matters.
- Define the smallest allowed change, compatibility constraints, and acceptance tests.
- Request a regression test that fails before the fix and passes after it.
- Create a Git checkpoint and inspect the complete diff.
- Rerun the original failure, then focused, neighboring, and required project checks.
- If the fix fails, provide the new evidence and revisit the diagnosis rather than layering guesses.
- Have a human decide whether the explanation, patch, and verification meet the requirement.
Frequently asked questions
Should I paste the entire repository into an AI assistant?
No. Begin with the failing path, relevant files, reproduction, and requirements. Add context when the assistant identifies a dependency or missing assumption. This keeps the investigation focused and limits exposure of unrelated or sensitive code.
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Can an AI assistant prove that it found the root cause?
No. It can propose a cause and identify evidence, but you must test the hypothesis against the observed behavior. A fix is credible only after the original failure and relevant tests are rerun successfully.
When is an IDE debugger assistant preferable to a terminal agent?
Use the debugger-oriented workflow when the key evidence is a live call stack, frame, variable value, or instrumented execution. Use a terminal agent when repository searches, scripts, command-line services, or local test tools are central. The best choice is the tool that can access the evidence and reproduction your bug requires.
What should I do when the assistant suggests a large refactor?
Restate the scope and ask for the smallest change that satisfies the requirement. Require an explanation of affected behavior and a regression test. Review and reject unrelated formatting, dependency, or architectural changes.
Is a passing build enough to close the bug?
No. Reproduce the original failure, verify the expected behavior, run the regression and neighboring tests, and complete the project’s required checks. A build can pass while a runtime, data, authorization, or user-facing behavior remains wrong.
Frequently Asked Questions
Should I paste the entire repository into an AI assistant?
No. Begin with the failing path, relevant files, reproduction, and requirements. Add context when the assistant identifies a dependency or missing assumption, while keeping secrets and unrelated private data out of the prompt.
Can an AI assistant prove that it found the root cause?
No. It can propose a cause and identify evidence, but you must test the hypothesis against observed behavior and rerun the original failure and relevant tests.
When is an IDE debugger assistant preferable to a terminal agent?
Use a debugger-oriented workflow when live call stacks, frames, variable values, or instrumentation are central. Use a terminal agent when repository searches, scripts, command-line services, or local test tools are central.
What should I do when the assistant suggests a large refactor?
Restate the scope and request the smallest change that satisfies the requirement. Require an explanation of affected behavior and a regression test, then reject unrelated changes.
Is a passing build enough to close the bug?
No. Reproduce the original failure, verify the expected behavior, run regression and neighboring tests, and complete the project’s required checks.
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