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I avoid “AI slop code” by treating every generated patch as an untrusted proposal: I check it against the project’s requirements and conventions, read the full diff, test the behavior independently, inspect security and dependency changes, and make sure a human developer owns the result. “AI slop” is an informal label, not a claim that AI-written code is inherently poor; the practical concern is code that looks plausible but is wrong, unsafe, hard to maintain, or out of step with the project.
Give the assistant enough context to solve the right problem
Before asking for code, state the behavior you need, the constraints that matter, and what should not change. Include authoritative project context where it is relevant: the README or design documentation, nearby implementation, established patterns, and tests. Tell the assistant which materials to follow when sources conflict.
Then compare the proposed change with the actual requirement and architecture. A polished explanation or plausible-looking implementation is not evidence that it solves the intended problem. GitHub’s guide to reviewing AI-generated code recommends checking requirements, project purpose, and design patterns, and using project materials as context.
Read the complete diff, not just the summary
Inspect every changed file, including tests, configuration, lockfiles, scripts, and permission declarations. For each change, ask whether it is necessary, understandable, and consistent with the codebase. Look for ignored constraints, incorrect assumptions about APIs, logic errors, naming that obscures intent, and missing edge cases.
A change that is difficult to follow is a warning sign even if it appears to work. GitHub recommends considering readability and maintainability—and whether a patch would be easier and safer to rewrite than to refactor. Microsoft makes the same core point for Windows development: “AI tools don’t remove the need for code review. They change what you’re reviewing, not whether you review.” Its examples about file paths, capabilities, and user-facing errors are platform-specific; apply them where they fit rather than treating them as universal APIs.
Use tests and static analysis as evidence, not a guarantee
Run the project’s existing tests and static checks, confirm the change compiles, and investigate new failures or warnings. GitHub’s guidance is direct: “Always run automated tests and static analysis tools first.” Passing compilation alone does not show that the behavior is correct, and a green test run is only as useful as the tests being run.
Add or adapt tests for the requested behavior and meaningful boundary or negative cases. Review AI-written tests just as carefully as implementation. OWASP warns against changes that delete tests, weaken assertions, replace the unit under test with mocks, or simply assert the generated code’s own assumptions. For security-sensitive behavior, write or obtain tests independently and consider adversarial testing; passing tests do not establish security.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsCheck dependencies, security, and privacy
When a patch adds or changes a dependency, verify the package’s identity, publisher, maintenance status, and license compatibility, and check for known vulnerabilities. Do not accept a package merely because the assistant named it confidently.
Rank #3
Review input validation, credential handling, file access, permissions, and error messages for the risks relevant to your application. Microsoft’s Windows checklist highlights avoiding hard-coded credentials, constraining file paths and capabilities, using HTTPS, and preventing user-facing errors from exposing internal details; treat these as Windows-oriented examples where appropriate, not blanket rules for every platform.
Do not put credentials or real customer data into prompts. Follow your organization’s policy for proprietary source code and any AI service you use. Security review should account for what data and capabilities the change exposes, not just whether the implementation appears functional.
Keep a human accountable for the accepted change
Automate repeatable checks in CI, such as style, security, code-quality, and coverage checks. Ask a teammate to review complex or sensitive work. Most importantly, assign a developer who understands the accepted code and is responsible for it. OWASP’s Secure Coding with AI guidance states: “AI-generated code must have a human owner.” AI-based review can help find issues, but it does not replace human understanding, approval, or accountability.
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A practical acceptance check
- Correctness: Does the patch meet the stated behavior, and do independently meaningful tests exercise it?
- Project fit: Does it respect the requirements, architecture, and local conventions?
- Maintainability: Can another developer explain and safely change it later?
- Security and privacy: Are inputs, secrets, dependencies, permissions, and data exposure addressed?
- Independent evidence: Do the checks test the change rather than merely ratify the same assumptions that produced it?
These are review questions, not a numerical score. The amount of scrutiny should match the risk of the change: a small low-impact edit and a security-sensitive feature do not need identical review, but neither should be accepted solely because an AI tool produced it or approved it.
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