The Tool Desk
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Why an AI tutor can miss the bug
It sees only part of the program
A chat tutor usually reasons from what you provide. If it cannot see the relevant call path, dependencies, configuration, inputs, logs, or runtime state, it has to infer what happened. A snippet that looks faulty may depend on code you have not shown—or may not be where the problem starts.
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Share the smallest example that still reproduces the issue, along with the exact input and full error or output. That gives the assistant something concrete to investigate rather than a vague symptom.
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The assistant needs to know both what happened and what should have happened. A program that crashes, returns the wrong value, or produces an unexpected display has different symptoms and may need different tests. State the expected behavior and the observed behavior separately.
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An unfamiliar API invites a plausible guess
If a library or internal SDK is unfamiliar, a model may reach for a similar-looking pattern that does not match the real API. Microsoft Principal Developer Advocate Waldek Mastykarz describes the risk: “The code looks plausible. That’s the trap.” His article discusses the issue particularly with proprietary and internal SDKs, so it is a reason to supply documentation—not proof that every suggestion for an unfamiliar library will be wrong. Read Mastykarz’s explanation at Microsoft for Developers.
Include the authoritative API reference and, if available, a known-good example. Ask the tutor to list what it is assuming about the API before it recommends a change.
A program can run and still be wrong
A visible error is only one kind of bug. The program may finish normally but calculate an unexpected result. In that case, compare expected and actual output for a small test case, then inspect intermediate values to find where they diverge. GitHub’s debugging guide distinguishes code that terminates with an error from code that runs but produces an unexpected result, and recommends checking suggested fixes in the program. See GitHub’s debugging guide.
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A patch can create another problem
A fluent explanation is not evidence that a change is correct. A proposed fix might address the visible symptom while changing behavior elsewhere, or introduce a new error. Treat each patch as a hypothesis: make one change, rerun the reproduction, and run the relevant tests. Check the language or library documentation when the behavior depends on a detail you cannot confirm.
What to send the tutor
Before asking for a fix, assemble the details that let someone else reproduce the problem:
- The language, runtime, and relevant library versions.
- The smallest code sample that still produces the issue.
- The exact input that triggers it.
- The complete error message or actual output.
- The output or behavior you expected instead.
- What you have already tried and what changed.
- Relevant official documentation or a known-good example, especially for unfamiliar APIs.
Do not paste passwords, credentials, or private code into a service unless your organization’s policies permit it.
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A better debugging exchange
Ask the tutor to separate diagnosis from code generation. This sequence helps make its reasoning checkable:
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- Restate the problem. Ask it to summarize the expected behavior, observed behavior, and any important unknowns. Correct misunderstandings before moving on.
- Offer a small number of hypotheses. Ask what evidence in the error, output, or code supports each possibility, rather than accepting an unexplained rewrite.
- Suggest a distinguishing test. Have it propose one small experiment or test case that could show which hypothesis is more likely.
- Make the smallest change. Only after checking the experiment should you ask for a minimal patch and an explanation of why it should work.
- Verify it yourself. Run the reproduction and relevant tests, compare the actual result with the expected result, and check documentation if an API detail remains uncertain.
If the answer skips from your symptom straight to a large rewrite, ask it to explain the diagnosis first. A rewrite is harder to verify because it changes more than one thing at once.
If your goal is to learn, ask for help without handing over the whole task
Fixing one defect and learning to diagnose the next one are different outcomes. You can ask for a hint, a question, or an explanation instead of a completed solution. For example, ask the tutor to explain a traceback, trace one variable through a loop, or help design a test without writing the fix.
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GitHub’s learning guide recommends setting up an assistant to teach concepts rather than simply supply solutions, and offers optional instructions for conceptual explanations without answers. That is product guidance, not independent proof that one prompting style always improves learning. Read GitHub’s guide to learning with Copilot.
After you solve the problem, explain the cause in your own words and try a nearby test case. If you cannot explain why the change works, ask the tutor to unpack that part rather than just provide another patch.
What the evidence says about AI assistance and coding skills
Anthropic reported a controlled study of 52 mostly junior software engineers who used Python regularly but were unfamiliar with the Trio library. Participants implemented two Trio features, with one group using an online assistant that could access their code and generate correct code if asked, and another hand-coding. On a later quiz, the AI group averaged 50% and the hand-coding group 67%; Anthropic reported the difference as statistically significant (Cohen’s d=0.738, p=0.01). The largest score gap was on debugging questions. The groups’ roughly two-minute difference in task completion time was not statistically significant. Read Anthropic’s study and its methodology.
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Those results concern a specific learning task, library, and participant group; they do not show that AI universally lowers coding ability or that every tutor fails at debugging. Anthropic’s qualitative analysis associated heavy delegation and AI-led debugging with lower quiz averages, while conceptual inquiry and explanation-oriented patterns appeared among higher-scoring groups. The authors caution that this analysis does not establish that interaction style caused the score differences.
The practical distinction is between using AI to replace your reasoning and using it to support a process you can check. A study result is not a verdict on every use: the assistant may help you explore a hypothesis, but the program and its tests still have to establish whether the fix works.
Choose a tutor by the workflow it supports
For debugging and learning, compare how a tool handles these practical needs rather than assuming a product label guarantees a good result:
- Project context: Can it see the relevant files and receive useful runtime feedback, or do you need to supply the evidence yourself?
- Documentation: Can you provide current, authoritative documentation or relevant workspace context when an API is unfamiliar?
- Learning mode: Can you request hints, questions, and explanations instead of complete code?
- Testing: Can you reproduce and test a suggestion easily in your editor and development environment?
- Privacy: Does the tool’s data handling meet your own or your organization’s requirements for the code you want to share?
These are evaluation criteria, not a ranking of current products. Capabilities and policies can vary, so check the particular tool and its current documentation.
Why confidence is not verification
OpenAI’s Help Center warns that ChatGPT can produce incorrect or misleading answers and may sound confident when wrong; it recommends checking important information against reliable sources. That warning supports verifying consequential claims, not a comparison of how often different products fix bugs. Read the OpenAI Help Center explanation.
For code, verification means running the smallest reproduction, checking the result against what you intended, and running relevant tests—not judging a patch by how polished its explanation sounds.
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