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I avoid AI slop code by treating every suggestion as an untrusted patch: I give the assistant real project context, inspect the complete diff, test behavior independently, check security and dependencies, and make sure a human owns the final change. “AI slop” is an informal label, not a verdict on how code was produced. I use it for observable problems: incorrect behavior, weak tests, needless complexity, security gaps, or code that does not fit the project.
1. Give the assistant the right context before it writes code
A vague request invites plausible code that solves the wrong problem. I describe the required behavior, constraints, and what must not change. Then I point the assistant to authoritative project materials: the README or design documentation, nearby implementation, established patterns, and relevant tests. GitHub’s guidance on reviewing AI-generated code recommends checking project purpose, requirements, design patterns, and project materials as context.
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I also identify which sources are authoritative when they conflict. For example, I might say that the documented API contract and existing behavior take precedence over an old comment. This gives me something concrete to compare the proposal against: the requested outcome and the architecture, not how polished the generated explanation sounds.
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I inspect the actual patch before accepting it. A change summary cannot reveal every implementation choice, and compilation cannot tell me whether the change is necessary, understandable, or consistent with the codebase.
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- Check every changed file, including configuration, lockfiles, tests, scripts, and permission declarations.
- Compare the implementation with the requested behavior and look for missed constraints, incorrect assumptions, fabricated or misused APIs, and unhandled edge cases.
- Ask whether each line is needed and whether another developer can explain and maintain it.
GitHub’s review guidance emphasizes readability, maintainability, naming, and whether a difficult-to-follow change would be better rewritten. 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 capabilities, paths, and user-facing errors are Windows-specific; the broader lesson is to review platform-specific security choices against the platform you actually target.
3. Test the behavior independently
I run the project’s existing tests and static analysis, confirm the change builds, and investigate new failures or warnings rather than treating a successful compile as proof of correctness. GitHub’s guidance says to run automated tests and static analysis first. Those checks are evidence, not a substitute for understanding the behavior.
I add or adjust tests for the requirement and meaningful boundary or failure cases. Most importantly, I review AI-written tests as carefully as production code. OWASP’s Secure Coding with AI guidance warns about deleted tests, weakened assertions, mocks that replace the unit being tested, and tests that simply assert the generated behavior. A test can pass while confirming the same mistaken assumption as the implementation.
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4. Check dependencies, security, and privacy
When a patch adds a package, I verify that the package exists and is the intended one, then check its publisher, maintenance, license compatibility, and known vulnerabilities. A plausible package name or generated explanation is not evidence that a dependency is safe to adopt.
I also look for security issues in the code itself: missing input validation, hard-coded credentials, overly broad permissions, unsafe file-path handling, and sensitive details exposed in user-facing errors. The precise controls depend on the language, platform, and application; Microsoft’s Windows guidance offers platform-specific examples rather than universal APIs.
I do not put credentials or real customer data into prompts, and I follow my organization’s rules for proprietary source. A code change can be functionally correct and still create a privacy or security problem through what was shared with an assistant or what the implementation exposes.
5. Keep a human accountable for the accepted change
I use CI to repeat style, security, quality, and coverage checks consistently, and I ask a teammate to review changes that are complex or sensitive. Automation can surface problems, but it cannot take responsibility for deciding that a patch fits the product and is safe to maintain.
Best Value
OWASP puts the accountability point plainly: “AI-generated code must have a human owner.” The person approving a change should understand it well enough to explain its behavior and accept responsibility for it; an AI-generated review or approval does not replace that judgment.
What counts as enough evidence?
There is no single score that makes an AI-assisted patch trustworthy. I look for several independent kinds of evidence, scaled to the risk of the change:
- The patch meets the stated requirement and fits the project’s design and conventions.
- I can understand the full change, including configuration and test edits.
- Relevant existing tests, new behavior tests, and static checks have run, with failures and new warnings investigated.
- Dependencies, inputs, secrets, permissions, and data exposure have been reviewed where relevant.
- A human developer has reviewed and taken ownership of the accepted result.
NIST’s SP 800-218A, published in 2024, augments the Secure Software Development Framework with practices for developing generative AI and dual-use foundation models across the software lifecycle. It is useful lifecycle context, but it is not a checklist specifically for reviewing ordinary AI-generated application patches.
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