How to Not Make Your AI Code Look Like AI Slop

Brandon WatkinsFounder, Flowlabz

There's a specific texture to AI-generated code that experienced developers can spot immediately. Overly verbose variable names. Redundant comments explaining what the code obviously does. Catch-all error handling that swallows exceptions silently. Helper functions created for operations that only happen once.

It's not that the code is wrong. It often works. It's that it doesn't reflect how a thoughtful engineer actually writes. And in a professional context, that matters. Code is read far more often than it's written. Code that looks like AI output signals to everyone who reads it that it wasn't carefully considered.

Here's how to use AI for development without producing output that gives the game away.

Understand Why AI Code Looks the Way It Does

AI models are trained to produce code that is correct and self-explanatory to a general audience. This means they default to maximum explicitness: long names, explanatory comments, defensive handling of every possible edge case, and abstraction patterns that make code readable in isolation but verbose in context.

A senior engineer writing for a team that knows the codebase doesn't need to explain what a userId is. AI doesn't know your team, so it explains everything, just in case. The result is code that reads like documentation rather than production software.

Prompt for the Code You Actually Want

The quality of AI code is almost entirely determined by the quality of your prompt. Vague prompts produce generic output. Specific prompts produce output that fits your codebase.

Tell it your conventions

Before asking for code, establish context: the language version, the framework, the style guide you follow, and the naming conventions in the existing codebase. "Write this in TypeScript using our existing service pattern, functions not classes, errors thrown not returned, no inline comments unless the logic is genuinely non-obvious."

You can go further. Paste a representative function from your codebase and say "match this style." AI is very good at pattern-matching to examples, use that.

Ask for less, not more

AI defaults to completeness. If you ask it to build a feature, it will build the feature plus validation, error handling, logging, and a test suite. Sometimes you want that. Often you want just the core logic, because you already have patterns for the rest.

Be specific about scope: "Just the transformation logic. I'll handle validation separately." This produces tighter, more purposeful output.

Treat AI Output as a First Draft, Not a Final Answer

The biggest mistake developers make with AI-generated code is treating it as done. It isn't. It's a starting point that needs the same review and refinement you'd apply to a junior developer's PR.

What to look for in review

• Unnecessary abstraction: Functions created for a single use case, interfaces defined for a type used once, constants for values that will never change.
• Defensive over-handling: Try/catch blocks around operations that can't throw, null checks on values guaranteed to exist, fallbacks for cases your business logic makes impossible.
• Verbose naming: getUserDataFromDatabase when the context already makes clear it's a database call. isUserAuthenticatedAndAuthorized when canAccess says the same thing in less noise.
• Redundant comments: // Increment counter by 1 above counter++. Comments that describe what the code does instead of why it does it.

The rewrite pass

After reviewing AI output, do a deliberate compression pass. Remove every comment that explains the obvious. Collapse helper functions that wrap a single line. Rename variables to match your codebase's idioms. This pass typically takes 10–20% of the time the AI saved, and produces code that reads as if you wrote it from scratch.

Use AI for the Right Parts of the Process

AI is strongest at boilerplate, data transformation, test generation, and translating logic from one language or framework to another. It is weakest at architecture decisions, performance-sensitive code, and anything requiring deep knowledge of your specific codebase context.

Use it for the tedious parts. Write the interesting parts yourself. The ratio of AI-assisted to human-written code isn't a metric of productivity. The quality of what ships is.

The Deeper Principle

The goal isn't to hide that you used AI. It's to own everything you commit. AI-generated code that you've reviewed, understood, and refined is your code. AI-generated code that you've copy-pasted and moved on from is a liability you haven't read yet.

The engineers whose AI use is invisible aren't hiding it. They're applying judgment to the output. That judgment is the skill that matters. The tool is just the tool.

If your app was built with AI tools and it shows in the interface as much as the code, read how to make an AI-built app look professional, or see our Design for AI-Apps service, where we design the fix directly in your codebase.