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Trust AI-generated code only to the extent that its behavior has been checked against your requirements, your project, and the consequences of getting it wrong. A convincing explanation or plausible-looking completion is not evidence that the code is correct. Treat each suggestion as a candidate: inspect it, test what matters, and raise the review bar when a change could affect security or be difficult to reverse.
Why plausible code can still be wrong
Code can look idiomatic and compile while misusing an API, omitting a project-specific assumption, or behaving incorrectly outside the narrow case that prompted the suggestion. In a 2024 study published in the Proceedings of the AAAI Conference on Artificial Intelligence, researchers reported that 62% of the code generated by GPT-4 in their evaluation contained API misuses. That figure describes the study’s dataset and setup; it is not a failure rate for every model, coding task, or current assistant. The result is useful as a warning about plausibility, not as a universal prediction. Read the AAAI-24 study, “Can LLM Replace Stack Overflow?”
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There is also a difference between completing the prompt and solving the project problem. A suggestion may satisfy the request as phrased yet conflict with surrounding code, dependencies, conventions, or constraints that were not included in the prompt. A 2024 study of coding assistants in practice identified inaccurate output, lack of trust or control, and insufficient context about project size among developers’ reasons for not using assistants. The Information and Software Technology study describes these as practical adoption concerns, not proof that every assistant fails in those ways.
When should you raise the review bar?
Trust is not an all-or-nothing choice. Match the effort spent verifying a suggestion to the impact of a mistake and how readily you could detect or undo it.
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- Lower impact: Routine boilerplate or a small formatting change may need a focused read and the ordinary checks you already use.
- Higher impact: Changes affecting authentication, authorization, secrets, input handling, data retention, or other consequential behavior warrant closer inspection and independent evidence. These are practical risk-management examples, not categories ranked by the cited studies.
- Hard to reverse: If a change could cause persistent data loss, expose information, or alter production behavior in ways that are difficult to roll back, do not treat a quick test or confident assistant explanation as enough.
This is a judgment framework, not a guarantee that any particular check makes code safe. The goal is to spend review effort where a missed defect would matter most.
How to verify an AI-generated change
The following sequence is an editorial synthesis for code review, not a protocol tested as a unit in any one study.
- Check the actual requirement. Compare the suggestion with the task, acceptance criteria, and relevant surrounding code. Look for assumptions the prompt may not have supplied, including project conventions and dependency behavior.
- Read the changed code and its assumptions. Trace inputs, outputs, error paths, and side effects. Do not substitute a fluent explanation for understanding what the implementation does. Research on trust in AI code-generation tools identifies communicating tool performance, configurable preferences, and indicators that help users evaluate how suggestions are produced as trust-supporting design concepts; none makes an individual suggestion correct. Microsoft Research’s trust study was published in 2023 and listed for FAccT 2024.
- Run relevant tests and inspect failures. Use the project’s existing tests and add or adapt cases for behavior the change introduces. A passing test establishes only that the behavior covered by that test passed under its conditions; it does not establish correctness for untested inputs or scenarios.
- Use independent security checks for security-sensitive work. Apply the project’s established security review and analysis practices rather than asking the same assistant to certify its own output. A 2024 IEEE Symposium on Security and Privacy evaluation reported evidence that the evaluated LLMs were not yet ready for automated vulnerability detection within its framework. That conclusion is tied to those systems and evaluation, not every future model or every security tool. See the IEEE evaluation.
- Check whether the result fits the real workflow. A benchmark result or isolated demonstration does not establish that a tool is useful in a developer’s codebase. Microsoft Research’s ICSE paper, published in April 2025, describes a study in which 17 professional developers used an AI-powered vulnerability detection and repair tool on their own projects; researchers examined usefulness, speed, trust, relevance, and workflow integration. The study design underscores why practical fit is a separate question from benchmark performance. Read the Microsoft Research study description.
What different checks can and cannot establish
Verification methods answer different questions. Treat them as complementary evidence rather than interchangeable stamps of approval.
| Check | What it can establish | What it does not establish by itself |
|---|---|---|
| Tests | Whether specified behaviors pass under the test conditions and inputs. | Correctness for behavior the tests do not cover. |
| Static analysis | Whether code matches patterns or rules the analyzer checks. | That every flagged issue is exploitable, or that unflagged code is free of defects. |
| Security review | Whether implementation choices fit the project’s context and threat assumptions, when reviewers have the needed information. | A universal guarantee that no vulnerability exists. |
| AI-generated security judgment | A suggestion or lead that may help a reviewer investigate. | Independent assurance that the code is secure; vulnerability-detection evaluations caution against relying on it as an automated verdict. |
Google Research’s 2024 work on trust in AI-powered developer tooling also addresses how trust should be designed and understood in developer tools. It is useful context for treating transparency and user judgment as part of the workflow, rather than equating confidence with reliability. Read the Google Research publication.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Deciding whether to accept, revise, or reject a suggestion
- Accept when the change matches the requirement, fits the surrounding project, and the checks appropriate to its impact provide adequate evidence.
- Revise when the basic approach is useful but an assumption, edge case, project convention, or test is missing. Make the correction explicit and verify the revised behavior.
- Reject when you cannot explain the behavior, the suggestion conflicts with requirements, it introduces unexamined risk, or you lack a reliable way to verify a consequential change.
The practical standard is not whether the assistant sounds certain. It is whether you can account for what the code does and have evidence proportionate to the cost of being wrong.
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