Use an AI coding assistant to draft or explore code, then review each change against the task, the project’s conventions, and checks suited to its impact. You do not need to assume every suggestion is wrong—or accept one just because it looks plausible. Keep a change only when you can explain what it does and verify that it is appropriate.
Should you accept an AI code suggestion?
Accept it when it solves the stated problem, fits the project, and you understand its behavior. Ask for a revision or dismiss it when it is unrelated, unclear, or introduces risk without a good reason. GitHub says Copilot inline suggestions can be accepted, dismissed, or ignored, and puts responsibility on users to review and validate suggestions before accepting them (GitHub Copilot inline suggestions).
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A suggestion can look convincing and still be inaccurate, incomplete, insecure, or out of step with what you intended. Inline suggestions may also lack the broader context needed to assess architectural consequences. Treat generated code as a proposal, not as evidence that the problem is solved.
How to check AI-generated code without turning review into a second project
1. State the job before asking for code
Write down the desired behavior and any important constraint in a sentence or two. Include relevant repository instructions, examples, or documentation when they affect the solution. GitHub recommends comparing generated code with the requirements and project design patterns; project documentation and recent pull requests can help provide that context (Review AI-generated code).
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2. Check fit before judging cleverness
Ask whether the change addresses the task, follows local conventions, and is small enough to understand. Elegance is not a reason to keep an unrelated change. Give closer attention to edits that affect architecture, permissions, data handling, or security: a suggestion shown in one place may not account for how the wider system works.
3. Run checks that match the change
Run the relevant tests and static analysis, and investigate new warnings or failures. If the project has CI checks for style, linting, security, code quality, or coverage, use them. GitHub cites CodeQL or similar scanners and Dependabot as examples of supporting tools. Automated checks complement human review; a passing test suite does not by itself prove that the code meets the intended behavior.
4. Scale review to impact
For a small, reversible change, a focused diff review and the relevant tests may be enough. For complex or sensitive work, examine edge cases, security behavior, data and permission boundaries, and maintainability; bring in a teammate when appropriate. GitHub likewise recommends collaborative review for complex or sensitive changes and attention to functionality, security, and maintainability (GitHub’s review guidance).
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5. Treat agent actions as real actions
An assistant that can edit files or run commands has a different risk profile from one that only offers a snippet. Understand what access and approvals the tool has, inspect the resulting changes, and read commands before running them. GitHub warns that terminal commands can be destructive if used incorrectly (Responsible use of GitHub Copilot Chat).
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Controls vary by product. OpenAI describes constrained execution, network policies, human approval for higher-risk actions, and logs in its account of Codex deployment (Running Codex safely at OpenAI). Check the documentation and settings for the assistant you use rather than assuming it has the same boundaries.
How much should you trust an AI coding assistant?
Trust it as a tool for generating, explaining, or exploring options—not as the final authority on correctness. GitHub notes that suggestions can be inaccurate or inappropriate and may lack context; its guidance also discusses variation by language and training-data representation (GitHub’s inline-suggestion guidance). Agent-based review has its own limits: it can miss problems, raise false positives, or propose flawed fixes (GitHub Copilot Agents).
The more consequential the change, the more evidence you should require. OpenAI’s Codex announcement says users still need to manually review and validate agent-generated code before integration and execution (Introducing Codex). That is a reason to keep human judgment in the workflow, not a reason to distrust every generated line equally.
When is it reasonable to stop reviewing?
Stop when the change matches the requirement, is understandable, and passes the checks appropriate to its impact. If one of those conditions is missing, request a revision, investigate further, or reject the suggestion. Once a low-impact change’s behavior is clear and verified, repeatedly asking for alternate explanations usually adds little.
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