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When an AI coding assistant gives you a wrong answer, pause before asking it for another patch. Reproduce the failure, preserve the current state, and gather evidence about what actually happens. Then ask the assistant to diagnose the problem without editing files, test its diagnosis, and verify the smallest change against the requirement—not just against a green test result.
An AI assistant is useful for generating hypotheses, but it cannot decide on its own that a bug is fixed. “Wrong” can mean a bad diagnosis, a symptom-only patch, a test that expects the wrong behavior, or advice that does not fit your project’s versions and conventions. The workflow below helps you tell those cases apart and reduce the risk of accepting a plausible but incorrect fix.
Why can an AI assistant give the wrong debugging answer?
A plausible explanation is not necessarily a correct one. OpenAI describes hallucinations as plausible but false statements and notes that incentives to answer can encourage guessing instead of acknowledging uncertainty. Its Model Spec guidance says an assistant should indicate uncertainty or ask for clarification rather than state uncertain information confidently.
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Microsoft Research reports that conversational debugging assistants may make assumptions, skip root-cause localization, and close an exchange too quickly with a proposed fix. That is why another patch is not automatically the right next step. Keep the exchange open until you have reproduced the behavior, examined evidence, and tested the suspected cause.
OpenAI’s 2025 SimpleQA table illustrates why uncertainty matters, but it is not a measure of coding-assistant performance: for gpt-5-thinking-mini, the table reports 52% abstention, 22% accuracy, and 26% error; for o4-mini, it reports 1% abstention, 24% accuracy, and 75% error. Those figures apply to those models on that evaluation—not to all models, coding questions, or debugging tasks.
What should I save before asking for another fix?
Preserve the state you are trying to understand. If the assistant has already made changes, inspect the diff before asking it to make more; otherwise you may lose the distinction between the original bug and a newly introduced one.
- The current diff or other record of changed files.
- The exact command or action that reproduces the failure, including the input used.
- The full error message, stack trace, relevant logs, and failing test output.
- The expected behavior and the behavior you actually observed.
- Relevant runtime, dependency, and configuration versions.
Visual Studio Code’s official guidance recommends checkpoints and reviewing changes before integration. A workspace restore is not a universal undo: it does not reverse terminal commands, network requests, deployments, or changes made in external services. Treat those side effects separately. If an agent has run commands or touched an external system, determine what happened before resetting files and assuming the environment is back to its prior state.
How do I reproduce the failure before diagnosing it?
Start with the smallest existing reproduction and record what happens before changing code. The baseline helps you distinguish a pre-existing failure from a regression and shows whether the symptom is deterministic, environment-specific, data-dependent, or intermittent.
- Use the project’s existing reproduction. Run the relevant test or repeat the documented steps with the same input and environment. Do not invent a new test command; use the one your project already defines.
- Record the result. Capture the actual output, error, and any relevant state or logs. Note whether the failure happens every time or only under particular conditions.
- Compare expected with actual behavior. State the requirement separately from the observed result. A test’s expected value is not automatically the requirement.
- Keep the original failing case intact. Do not change the test or its expected values just to get a green run. The official testing guidance recommends recording existing failures so they can be distinguished from failures introduced later.
If the failure cannot be reproduced, do not treat an apparently successful patch as proof. First narrow down what differs between the reported case and the current run: inputs, data, runtime, configuration, timing, or environment.
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What should I ask the assistant to do before it edits code?
Give it a bounded, relevant evidence set and ask for a diagnosis only. A vague request invites a vague answer; exact errors, failing tests, and the relevant code path give the assistant something concrete to reason about.
Include the smallest relevant code path, exact failing input, expected and actual behavior, test output, and relevant versions or configuration. If a requirement or project documentation defines what correct behavior means, include that too. Avoid pasting unrelated files: more context is not useful if it obscures the facts that distinguish one explanation from another.
A useful prompt is:
Diagnose this failure without editing files. State the observed facts, list your assumptions, propose two or three hypotheses, and specify the smallest test or runtime observation that would distinguish them. Do not recommend a fix until the failing behavior and expected behavior are clearly separated.
Review the assistant’s assumptions and ask what observation would disprove each hypothesis. A diagnosis that cannot be tested is not yet a reliable basis for a code change. IBM emphasizes providing exact context and failing tests; Microsoft Research’s findings about premature closure make it sensible to ask for evidence and alternatives before accepting a fix.
How do I test the diagnosis rather than just the patch?
Choose an observation that would separate the assistant’s hypothesis from plausible alternatives. Depending on the bug, that may be a minimal reproducer, a focused regression test, a log statement, a debugger watch, or a trace of the relevant execution path.
- Write down what the hypothesis predicts. Identify the input, state, or execution path that should produce the failure if the explanation is right.
- Choose the smallest discriminating check. Prefer a test or observation that would behave differently under competing explanations, rather than one that merely repeats a broad suite.
- Compare the result with the requirement. A patch can satisfy a test while violating the behavior users or project specifications require.
- Make the smallest change that addresses the supported cause. Keep unrelated cleanup and broad rewrites out of the same change so that its effect remains understandable.
- Run the focused check again. Inspect its actual output before moving on; do not rely solely on the assistant’s summary.
For a bug involving live program state, static code reading may not be enough. IBM describes AI-debugging systems that can connect language models to standard debuggers such as GDB, LLDB, and Python’s pdb. A debugger can expose values and execution paths, but it does not decide what they mean: confirm the observed state and whether it explains the behavior.
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For a visual defect, reproduce the user-visible problem in a faithful environment instead of relying only on source-level reasoning. For concurrency, networking, or distributed behavior, preserve the smallest useful trace, including timestamps, request identifiers, and state transitions where available. These observations can show whether the proposed root cause matches what happened.
What if the tests pass but the bug is still there?
Treat a green result as evidence about the tests that ran—not proof that the original problem is fixed. The first question is whether the passing test reproduces the user-visible failure and checks the requirement you care about.
- Is it the same failure? Confirm that the test uses the relevant input and exercises the affected path, rather than a nearby or simplified case.
- Does it assert the right behavior? Check the requirement behind each assertion. A test can pass because its expected value is wrong.
- Are mocks hiding the problem? A mock may replace the dependency or behavior where the defect actually occurs.
- Does the environment match? Differences in data, configuration, runtime, or dependency versions can make a passing test irrelevant to the reported case.
- Is the bug outside the test’s view? Visual, concurrent, stateful, and integration failures may not appear in an isolated unit test.
When the test does not capture the original defect, add a failing regression test or a concrete runtime observation that does. Do that before accepting another patch. The point is not to accumulate more tests indiscriminately; it is to make the real failure observable under the conditions that matter.
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How can I tell whether an AI-generated test is trustworthy?
Review the test as carefully as the implementation. A generated test can be internally consistent and still encode broken behavior, so passing it does not independently establish correctness.
- Compare its assertions with the stated requirement, including boundaries and error cases.
- Inspect deleted tests, changed expected values, and weakened assertions.
- Check whether new mocks bypass the behavior the test is meant to exercise.
- Make sure the test does not merely assert the assistant’s generated output.
- Add important negative and boundary cases independently instead of assuming the assistant covered them.
OWASP’s Secure Coding with AI Cheat Sheet warns that “100% passing means nothing if the tests assert broken behavior.” Coverage can show what executed; it cannot establish that the test’s expectations are correct. A human-written test is not automatically correct either, but independently checking the requirement helps prevent the implementation and its test from repeating the same mistaken assumption.
In what order should I expand verification?
Begin with the narrowest relevant check, then broaden verification to catch interactions and other failure classes. The project’s existing commands and conventions determine the exact commands to run; a successful result applies only to checks that actually executed.
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- Focused regression test: verify the specific failure and its expected behavior.
- Related test suite: check nearby components and interactions.
- Broader suite: look for regressions outside the immediate code path.
- Static checks: run applicable type checks, linters, and static analysis.
- Integration, end-to-end, or visual checks: use the level of verification the bug requires.
- Security and dependency checks: include them when code or changes affect security-sensitive behavior.
GitHub recommends checking compilation, tests, warnings, security tools, dependency issues, and alignment with requirements. The official testing guidance also emphasizes inspecting actual execution results, including failures and skipped tests. Do not call a check verified if it was skipped, blocked, or never run, and distinguish a partial run from a complete one when reporting results.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsHow do I review AI-generated code for security and privacy risks?
Review security separately from functional correctness. A patch can pass its tests and still introduce an injection flaw, hardcoded secret, missing validation, insecure dependency, or risky build or deployment change.
Keep a human reviewer responsible for decisions involving authentication, authorization, input validation, cryptography, data integrity, and deployment. Pay particular attention to package scripts, CI workflows, Docker files, build configuration, and deployment automation: those files can run automatically in trusted contexts. Review the change and its consequences rather than assuming a small-looking diff is harmless.
Inspect the context your assistant can access or send to its provider. OWASP warns that some tools may send broader project context than the current file. Exclude or redact secrets and sensitive directories where appropriate, including environment files, private keys, credentials, personal data, and proprietary architecture. Independent security analysis and code review add useful safeguards, but neither a scan nor a passing suite alone proves the behavior is secure.
Security-critical review checklist
- Check invalid input, boundaries, negative cases, expired tokens, malformed payloads, and concurrency conditions relevant to the change.
- Look for deleted tests, weakened assertions, changed expected values, and mocks that bypass real behavior.
- Review secrets and the context shared with the model provider; exclude or redact sensitive material when appropriate.
- Use security analysis and human review, especially for authorization, cryptography, data integrity, and deployment changes.
Which debugging approach gives me more control?
The right approach depends on what you need to learn. The comparisons below describe practical trade-offs, not a claim that one tool or workflow is best in every project.
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| Diagnosis-only prompt vs. edit-capable agent mode | A diagnosis-only exchange surfaces assumptions and hypotheses before code changes; an agent can act on a chosen hypothesis. | Diagnosis-only gives more control and a clearer audit trail; edit-capable mode may be faster but makes review of changes and side effects important. |
| Targeted regression test vs. full-suite run | A focused test isolates the reported behavior; a full suite can reveal wider interactions. | The focused check is quicker to interpret; the broader run gives wider coverage but can make the cause of a failure less obvious. |
| Static review vs. runtime evidence | Static review examines code and structure; runtime evidence reveals observed state and behavior. | Static reasoning may miss timing or environment effects; runtime evidence requires a relevant reproduction and careful interpretation. |
| Local tools vs. cloud tools | Either may help inspect code or run checks, depending on the project setup. | Consider what context is available to the tool and the privacy, trust, and network boundaries involved; no single privacy property follows from the label alone. |
| Human-written tests vs. AI-generated tests | Generated tests can speed up test creation; independently written expectations can provide a separate check on the assistant’s assumptions. | Neither origin guarantees correctness. Verify assertions against requirements, and inspect mocks and changed tests. |
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What should I do if the assistant keeps changing the wrong thing?
Stop giving it edit permission until you can state the failure and the evidence clearly. Revert or isolate changes only after inspecting their effects, then return to a diagnosis-only exchange focused on one observed behavior.
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- Restate the requirement and the exact failing behavior in separate sentences.
- Provide the minimal relevant code, reproduction, error, and version details.
- Ask for assumptions, competing hypotheses, and a discriminating check—without a patch.
- Reject explanations that do not account for the observed failure, or request the missing evidence they need.
- After one cause is supported, ask for a minimal change and review the resulting diff before running checks.
If the conversation repeatedly proposes fixes without explaining how they address the reproduced behavior, do not keep cycling through edits. Improve the evidence, reduce the scope, or use a debugger or another independent review path. The developer remains responsible for deciding whether the proposed change matches the requirement.
Frequently Asked Questions
Does an AI assistant hallucinate every time it gets a coding answer wrong?
No. A wrong answer can result from missing project context, an incorrect assumption, or a test that encodes the wrong expectation; it need not be a fabricated fact. The useful response is to identify which evidence the explanation fails to account for.
Should I paste my whole repository into a coding assistant?
Not automatically. Some tools may transmit broader project context than the file you are viewing, so check the tool’s context and privacy behavior and avoid exposing secrets or sensitive material.
Can a debugger prove the assistant’s diagnosis is right?
A debugger can show live values and execution paths, but the observation still needs interpretation against the requirement. A value being present does not by itself establish that it caused the user-visible defect.
How should I report a fix if only some checks ran?
Name the checks that actually completed and distinguish them from checks that were skipped, blocked, or not run. That gives reviewers a useful boundary around what the result establishes.
