What Is AI Slop? (And How to Keep It Out of Your Codebase)

Where the word came from

"Slop" used to mean pig feed. Now it mostly means the stuff AI produces when nobody is checking: the bland blog post padded to 2,000 words, the stock-photo-style image of a man with seven fingers, the LinkedIn post that opens with "In today's fast-paced world". The term caught on in 2024 as people looked for a word for low-effort generated content flooding search results and social feeds, and by the end of 2025 Merriam-Webster had picked "slop" as its word of the year.

The definition most people use is simple. AI slop is content made with AI, at volume, where nobody has taken responsibility for whether it is any good. The AI part isn't the problem. The missing human is.

What slop looks like in writing

You have read plenty of it. The tells are easy to spot once you know them:

• An opening that restates the question instead of answering it
• Every paragraph the same length, every list exactly three items
• Words like "delve", "landscape", "robust" and "seamless" doing no work
• Confident claims with no source, or a source that doesn't exist
• A conclusion that summarises what you just read, then tells you the topic is "more important than ever"

None of these is wrong on its own. Together they produce text that is technically readable and says almost nothing.

What slop looks like in code

This is the part we see every week. Code has its own version of slop, and it is harder to spot because it usually runs.

Slop code is code that works in the demo and nobody on the team can explain. Typical signs:

• Three different libraries doing the same job because each prompt pulled in a new one
• Functions that are 400 lines long with comments that just repeat the code
• Error handling that catches everything and logs nothing
• API keys hardcoded in the frontend
• Tests that were generated to pass, not to catch anything
• Packages that don't exist. Models sometimes invent plausible package names, and attackers have started registering those names with malicious code inside. The security community calls this "slopsquatting".

The app looks finished. Then you try to deploy it, add a second developer, or handle 500 users at once, and the cracks show.

Why it happens

AI tools are very good at producing something that looks right. They are not accountable for whether it is right. When the person prompting can't judge the output, either because they aren't a developer or because they are moving too fast to read it, slop is what you get by default.

Speed makes it worse. If you can generate a feature in ten minutes, it feels wasteful to spend an hour reviewing it. So nobody does, and the unreviewed code piles up.

Is all AI-generated work slop?

No. We use Claude Code, Cursor and the rest every day. The difference between slop and useful AI output is whether someone with the right knowledge looked at it, understood it, and was prepared to put their name to it.

A good test: could you explain this to a colleague without opening the AI chat again? If the answer is no, you have a problem waiting to happen.

How to avoid shipping slop

With writing, the fix is boring: someone has to actually edit it. Not skim it, edit it. Click every link. Look up every number, because models are happy to quote a 2021 figure as if it were current. And if a sentence sounds like nobody would ever say it out loud, delete it.

Code takes a bit more discipline. Read the diff before you merge, including the dull ones, since that's usually where the hardcoded key turns up. When a new package appears, open its npm or PyPI page and check it's real, has more than a handful of downloads and has been touched this year. Put a security scan like Trivy or npm audit in your CI so it runs on every push rather than whenever someone remembers. Write the tests for checkout and login yourself; let the AI do the rest if you like.

And if nobody on the team can read the code at all, get someone outside to look at it before launch day, not after the first outage.

Where VibeGO fits

Most of the apps we're asked to deploy were built quickly with AI tools, and most of them contain some slop. That's normal. Our job is to find it before your users do: we review the code, fix what will break in production, and explain what we changed so you are not dependent on us afterwards.

If you've built something with Lovable, Bolt, Replit or Claude Code and you're not sure what is under the bonnet, get in touch. We'll tell you honestly whether it's ready.