Guide

How to Humanize AI-Generated Code: Make It Read Like Your Own (2026 Guide)

AI assistants write code that works but reads like nobody on your team wrote it: over-commented, generically named and inconsistent with the rest of the project. Here is how to clean it up so it fits in — by hand or automatically.

Why AI code stands out in a codebase

The problem with AI-generated code is rarely correctness. It's that the code doesn't match the conventions around it, so reviewers spend time on noise instead of logic. The common tells:

Comments that restate the code — # Loop through each item above a for loop adds nothing a reader can't see
"Step 1:", "Step 2:" scaffolding — useful while the model was reasoning, noise once the code is in your repo
Naming that ignores your project — camelCase locals in a snake_case Python codebase, or the other way round in JavaScript
Needless temporaries — result = a + b followed immediately by return result
Docstrings that repeat the function name — """Calculate the total.""" on calculate_total()

Step 1: Delete comments that only restate the code

Keep comments that explain why — a business rule, a link to an issue, a workaround. Delete comments that explain what when the line below already says it. A good test: if every meaningful word in the comment already appears in the code, the comment can go.

Step 2: Strip the scaffolding

Models number their steps because it helps them reason. In a pull request, # Step 3: Return the result is clutter. Remove the numbering; if the remaining text still adds information, keep it as a normal comment.

Step 3: Apply your naming convention

Python uses snake_case for locals, JavaScript and Java use camelCase, and C codebases each have their own house style. Rename AI-introduced locals to match. Leave function names and parameters alone unless you control every caller.

Step 4: Remove needless indirection

Inline variables that are assigned and immediately returned, and drop docstrings that only repeat the function name. Keep docstrings that document parameters, return values or edge cases.

Step 5: Review and test like any other change

Cleaned-up code is still code you're responsible for. Read it, run your tests and let your linter and formatter (Black, Prettier, ESLint, clang-format) have the final say on formatting.

What it looks like

Here is a typical AI draft and the same code after Code Humanizer's Idiomatic preset at 100%. The logic is unchanged.

AI draft

import json def load_user_settings(file_path, default_theme=None): """Load the user settings.""" # Step 1: Open the file and read the contents with open(file_path) as f: rawData = json.load(f) # Step 2: Check if the theme is set if rawData.get("theme") == None: rawData["theme"] = default_theme # set the theme to the default theme # Step 3: Create the user settings dictionary userSettings = {"theme": rawData["theme"], "language": rawData.get("language", "en")} return userSettings

After Code Humanizer (Idiomatic, 100%)

import json def load_user_settings(file_path, default_theme=None): """Load the user settings.""" # Open the file and read the contents with open(file_path) as f: raw_data = json.load(f) if raw_data.get("theme") is None: raw_data["theme"] = default_theme return {"theme": raw_data["theme"], "language": raw_data.get("language", "en")}

Pick the preset that matches your team

Idiomatic — naming conventions and language idioms, keeps docstrings
Minimalist — also drops docstrings that only repeat the function name
Documented — keeps your comments and docs, only removes scaffolding and noise
Enterprise — explicit and conservative, no structural changes

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A note on coursework

Code Humanizer is a readability and style tool. It isn't designed to hide who wrote code, and using it to break a course's academic integrity rules is against our Terms of Service. If your course allows AI assistance, comparing an AI draft with a cleaned-up version is a good way to learn idiomatic style.

Summary

To make AI code read like your own: delete comments that restate the code, strip step scaffolding, apply your naming convention, remove needless temporaries and trivial docstrings, then review and test. Code Humanizer automates the first four for Python, JavaScript, Java and C/C++.