Stop AI Slop: 22 Code and Writing Patterns to Catch

To stop AI slop, look for repeated patterns rather than treating any single style choice as proof: in code, check for needless complexity, unverified dependencies, generic error handling, and missing tests; in writing, check for vague claims, repetitive structure, and unsupported detail. Keep useful drafts, explanations, and boilerplate when they are accurate, tested, specific to the task, and reviewed by a person who can take responsibility for them.

What “AI slop” means in practice

“AI slop” is an informal label for low-quality or misleading output, not a technical category with a universal test. The phrase can describe code that appears plausible but fails under real conditions, or writing that sounds polished while saying little, repeating itself, or presenting false claims confidently. Human work can have these problems too, and AI-assisted work can be useful. The practical question is whether the result meets the requirements and has been checked.

Patterns are warning signs, not proof of how something was made. A repeated phrase, unusual variable name, or smooth tone cannot reliably identify authorship. Review the work itself: verify behavior, evidence, originality where relevant, and fit for its audience.

Code patterns worth catching

1. Unnecessary complexity: extra wrappers, layers, abstractions, or configuration that do not solve a stated problem. Ask what each part contributes.
2. Duplicated logic: similar blocks copied across functions instead of a clear shared implementation. Duplication may be intentional, but it should not create inconsistent fixes.
3. Overly broad exception handling: catching every error and then ignoring it, printing a generic message, or returning a misleading success value. Preserve useful context and handle expected failures deliberately.
4. Invented or mismatched APIs: calls to functions, package methods, or options that do not exist in the project’s actual versions. Check official documentation and run the code.
5. Unnecessary dependencies: new packages for a small task, without considering maintenance, compatibility, licensing, or security review. Confirm that a dependency is needed and approved.
6. Hard-coded assumptions: fixed paths, dates, locales, limits, or environment details that fail outside the example machine. Make assumptions explicit or read them from appropriate configuration.
7. Weak input validation: trusting data from users, files, or services without checking its type, range, and format. Validate at the boundary where data enters the system.
8. Misleading names and comments: comments that merely restate a line, stale explanations, or identifiers that promise behavior the code does not deliver. Prefer accurate names and comments about non-obvious reasons.
9. Tests that only repeat the implementation: tests may exercise the happy path while missing edge cases, failures, and expected output. Add cases based on requirements, not just the code’s current shape.
10. Unreviewed generated changes: large edits, migrations, or configuration updates accepted because they look coherent. Inspect the diff, run relevant checks, and understand each consequential change.
11. Security-sensitive shortcuts: unsafe command construction, exposed secrets, weak permission checks, or unsafe handling of untrusted content. Treat these as review priorities; a passing demo is not a security assessment.

Writing patterns worth catching

1. Vague opening claims: broad promises such as “This changes everything” without a defined subject or evidence. Replace them with the actual point and its scope.
2. Repetition disguised as structure: several headings that make the same claim in slightly different words. Keep sections only when each adds a distinct answer, example, or qualification.
3. Generic transitions: phrases like “In today’s fast-paced world” that do not connect specific ideas. Start with the relevant fact or relationship instead.
4. Lists padded with near-duplicates: a long list whose entries overlap or lack criteria. Combine repeated items and explain how the remaining examples differ.
5. Unsupported precision: exact figures, dates, quotations, or named sources that cannot be traced. Verify them against reliable original material or remove them.
6. Confident but empty recommendations: advice such as “use the best tool” without explaining for whom, under what conditions, or why. State the decision factors and trade-offs.
7. Examples that do not prove the claim: illustrative scenarios presented as evidence of a general trend. Label examples clearly and find suitable evidence for factual conclusions.
8. Overgeneralization: treating a group, product, or situation as uniform. Add meaningful limits, exceptions, or a narrower claim.
9. Unnatural consistency: every paragraph having identical length, cadence, and sentence structure. Vary the writing where meaning calls for it, but do not manufacture quirks as a supposed detection trick.
10. Unnecessary summaries: repeating the introduction at the end without adding a conclusion or next step. Close with the decision, implication, or unresolved question that matters.
11. Polished language masking missing substance: fluent prose that never answers the reader’s question. Check each section against the stated purpose and remove sentences that add neither information nor useful context.

How to review without rejecting useful work

Use a two-part review: first check correctness, then check quality and fit. For code, establish what the change is supposed to do, inspect the diff, run appropriate tests, and review dependencies and error paths. For writing, identify the intended reader and claim, verify factual statements, and see whether examples support rather than decorate the argument.

• Ask for the source, test, or requirement behind a consequential claim.
• Test edge cases, not only the example that makes the output look successful.
• Check whether the result follows the project’s conventions and the reader’s actual needs.
• Keep a record of important decisions and unresolved assumptions.

Automated tools can help find formatting issues, run tests, check links, or flag suspicious similarities. They can also miss defects or produce false alarms. A detector score or stylistic impression is not a substitute for verification, and it should not be treated as conclusive proof of authorship.

What to keep—and what to change

Keep the parts that demonstrably help: a clear explanation, a useful outline, a routine code transformation, a test idea, or a concise first draft. Retain them only after checking factual accuracy, behavior, context, and ownership of the final result. In particular, preserve code that passes meaningful tests and follows project requirements, and writing that makes specific, supportable claims in an appropriate voice.

Change or discard material when it invents facts or APIs, hides uncertainty, duplicates existing work, violates project rules, or cannot be explained by the person responsible for it. Do not “humanize” questionable output by adding typos, slang, or arbitrary variation. Improve its substance instead: clarify the requirement, add evidence, simplify the implementation, or rewrite from verified notes.

Common misconceptions and limits

There is no reliable universal checklist of stylistic tells that proves a person or a machine produced a passage or program. Generic phrasing and repetitive structure are reasons to edit, not reliable authorship tests. Similarly, code that compiles is not necessarily correct, and prose that sounds authoritative is not necessarily true.

Review effort should match consequences. A low-stakes draft may need a quick factual and editorial check; changes involving sensitive data, security, or critical systems warrant appropriate specialist review and stronger testing. In every case, make the acceptance criteria clear before judging whether a result is good enough.

Frequently Asked Questions

What are signs of AI slop in code?
Common warning signs include nonexistent APIs, unnecessary dependencies, broad error handling, hard-coded assumptions, duplicated logic, and tests that miss realistic failures. Confirm each concern by inspecting documentation and running suitable checks.

Can you identify AI-written text by style alone?
No style pattern reliably proves authorship. Repetition, vague claims, or generic transitions can signal weak writing, but people produce them too. Evaluate evidence, clarity, and usefulness rather than guessing who or what wrote it.

Should I delete every AI-assisted draft?
No. Keep material that is accurate, appropriate, and verified. Revise or remove unsupported claims, incorrect code, and content that fails the task’s requirements.

How do I check generated code before using it?
Compare it with the actual requirements and project environment, verify APIs and dependencies, inspect the complete change, and run relevant tests. Review security-sensitive behavior separately when the consequences justify it.

Do AI detectors reliably catch AI slop?
Detectors can make mistakes and do not establish that content is false or machine-written. Use them, if at all, as one limited signal—not as a replacement for checking sources, behavior, and context.

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