Your AI Wrote Code: How to Stop It From Becoming a Hot Mess
Z
Zack Saadioui
8/12/2025
So, Your AI Wrote Some Code. Here’s How to Stop It from Becoming a Hot Mess.
Alright, let's be real. Using AI to generate code feels like a superpower. You type a prompt, grab a coffee, & poof – a whole chunk of logic appears out of thin air. It's fast, it's exciting, & honestly, it's changing how we build software. But here's the thing nobody tells you in the flashy demos: a lot of that AI-generated code is, well, a bit of a disaster waiting to happen.
It might work, sure. But it’s often messy, confusing, & an absolute nightmare to maintain down the line. We're talking about code that's brittle, hard to read, & architecturally questionable. Relying on it blindly is like building a skyscraper on a foundation of sand. Sooner or later, it’s gonna come crashing down.
I’ve been in the trenches with these tools, & I've seen the good, the bad, & the truly ugly. Turns out, the secret to using AI effectively isn't about letting it do all the work. It’s about learning how to manage it, guide it, & clean up after it. This is the guide I wish I had when I started. We're going to dive deep into how to take the raw, chaotic output of an AI & turn it into clean, robust, & genuinely maintainable code.
The Golden Rule: Treat AI as Your Junior Dev, Not a Senior Architect
This is the MOST important mindset shift you need to make. Your AI code assistant—whether it's Copilot, ChatGPT, or something else—is not a seasoned expert. It's a super-enthusiastic junior developer who's read every textbook & blog post on the planet but has zero real-world experience.
It’s great at churning out boilerplate, solving isolated problems, & even implementing known patterns. But it has no understanding of your project’s bigger picture. It doesn’t know about your architectural goals, your long-term vision, or that weird business logic you had to implement last quarter. Some sources are pretty blunt about it, saying AI can't do design or architecture; it just parrots back patterns it's seen online.
Think about how you’d work with a new junior dev:
You wouldn’t let them push code directly to production without a review.
You’d give them clear, specific instructions.
You’d check their work for quality, readability, & efficiency.
You’d expect to refactor & improve upon their initial drafts.
THAT is exactly how you need to approach AI-generated code. It’s a starting point, a draft, a helpful suggestion. It’s NOT the final product. The real magic happens when you, the experienced developer, apply your critical thinking & craftsmanship to its raw output. The human-AI partnership is the new paradigm; the AI amplifies your ability, it doesn't replace it.
Back to Basics: Why Clean Code Principles Are MORE Important Than Ever
For a while, some folks wondered if AI would make clean code obsolete. If a machine is writing it, who cares what it looks like? Turns out, the opposite is true. With the sheer volume of code AI can produce, clean code practices are our main line of defense against descending into chaos.
AI tools are notoriously bad at the fundamentals that make codebases livable.
Meaningful Naming is Your First Clue
One of the most immediate signs of AI-generated code is terrible naming. You’ll see variables like
1
data
,
1
temp_list
,
1
result1
, or
1
processed_item
. These names are technically functional, but they’re meaningless. They give you zero context about what the variable actually holds or represents.
A human developer would (hopefully) choose
1
customer_order_history
or
1
user_profile_image_url
. These names are self-documenting. When you’re scanning hundreds of lines of code, meaningful names are the signposts that keep you from getting lost. The AI is lazy with names; you can't afford to be. Take the extra five seconds to rename every vague, AI-suggested variable. It's one of the highest-leverage activities you can do for future maintainability.
Good God, The Structure
AI loves to write monolithic functions. It will happily generate a single, 300-line function that fetches data from an API, transforms it in three different ways, handles errors, updates the database, & then sends an email. It’s a marvel of procedural spaghetti.
This violates one of the most sacred rules of software engineering: the Single Responsibility Principle (SRP). A function should do one thing, & do it well. That 300-line monster should be broken down into 5-7 smaller, reusable functions like:
1
fetch_user_data(user_id)
1
transform_data_for_processing(api_data)
1
save_user_record(user_record)
1
handle_database_error(error)
1
send_notification_email(user_email)
The AI doesn't think about modularity or reuse. It just solves the immediate prompt. It's your job to step in & act as the architect, breaking down its massive code blobs into logical, maintainable components.
Readability & Simplicity
AI doesn't have to read its own code six months from now, but you do. It often produces dense one-liners or uses obscure syntax that, while clever, is hard to decipher. It might also introduce weird formatting choices or inconsistent indentation.
Good code is simple code. It flows logically. It’s easy to follow. If you have to stare at a line of code for five minutes to understand what it does, it’s bad code—I don't care if it works. Often, a more verbose, multi-line version of an AI's clever one-liner is VASTLY superior because it’s more readable for the rest of the team. You should also refactor deeply nested conditions & loops, which are a classic sign of overly complex logic that AI can spit out.
Mastering the Prompt: Garbage In, Garbage Out
If you're getting crappy code from your AI, the first place to look is your prompt. Vague prompts lead to vague, generic code. Better prompts lead to better code. It's that simple.
Think of it as writing a detailed ticket for your junior developer. Instead of saying:
"Write a function to upload a file."
Try something MUCH more specific:
"Generate a Python function using Flask for a secure file upload. The function should be named
1
upload_user_profile_picture
. It must validate that the file is a JPEG or PNG & is under 5MB. Use the
1
werkzeug.utils.secure_filename
to sanitize the filename. It should save the file to the
1
/uploads/images
directory. Ensure it follows PEP8 style conventions & include error handling for file-not-found & invalid file type scenarios. Add docstrings explaining the parameters & return value."
See the difference? We’ve given it:
The technology stack (Python, Flask)
A clear function name
Specific validation rules (file type, size)
Security requirements (
1
secure_filename
)
Coding standards (PEP8)
Directory structure
Error handling specifics
Documentation requirements (docstrings)
Crafting a detailed prompt like this takes an extra minute, but it can save you thirty minutes of cleanup on the back end. You're front-loading the work & guiding the AI to produce something that’s already 80% of the way to being clean & maintainable.
Your New Workflow: The "Generate, Refactor, Review" Cycle
You can't just plug AI into your old workflow. You need a new process that treats AI as a specific step with its own quality gates. Successful teams build a rhythm around this.
Step 1: The First Draft (AI Generation)
This is the fun part. You write your detailed prompt & let the AI do its thing. The goal here isn't to get perfect, production-ready code. The goal is to get a functional draft. You're using the AI to overcome inertia, to get the basic structure & logic down without having to type every single character yourself. It's a starting block.
Step 2: The CRUCIAL Refactoring Phase
This is where you, the human developer, earn your paycheck. AI output is a draft, not a finished product. You need to put on your code janitor hat & get to work. This isn't just about fixing bugs; it's a deep, structural cleanup.
Your refactoring checklist should include:
Simplify Complex Logic: Did the AI use a convoluted algorithm when a simple
1
for
loop would do? Simplify it. Remove redundant steps or unnecessary conditions.
Decomposition: Is this one giant function? Break it down into smaller, single-responsibility functions.
Naming: Go through every single variable, function, & class name. If it's not crystal clear, rename it.
Remove Duplication (DRY): AI often repeats itself. If you see the same 5 lines of code in two different places, extract them into a shared function.
Check for Performance Killers: This is a BIG one. AI is notorious for writing inefficient code. A classic example is making a database query inside a loop (the N+1 problem). A human developer should spot this immediately & refactor it to use a single, batched query before the loop.
Security Vulnerabilities: AI code is trained on a massive dataset, including a lot of insecure code from public repositories. You MUST manually check for common vulnerabilities like SQL injection, cross-site scripting (XSS), or insecure direct object references. Never trust the AI to write secure code out of the box.
Some teams even schedule regular "AI code cleanup" sessions to tackle this technical debt proactively. It’s like weeding a garden—do it often, & it’s manageable. Let it go for months, & you’ve got an overgrown jungle.
Step 3: Reinventing the Code Review for the AI Era
The code review process has to adapt. It’s no longer just about catching typos or logic errors. It's about ensuring the AI's contribution aligns with the project's standards.
Reviews for AI-assisted code should have a special focus on:
Architectural Alignment: Does this code fit our established patterns? Or did the AI introduce a completely new library or design pattern that we don't want to support?
"Why" over "How": The developer who used the AI should be able to explain why the code is the way it is. If their answer is "because the AI wrote it that way," that's a HUGE red flag. They need to understand the code they are committing.
Spotting AI Hallucinations: AIs can sometimes "hallucinate" & invent functions, libraries, or API endpoints that don't actually exist. A thorough review is needed to catch these.
Over-reliance Check: Is the developer just copy-pasting without thinking? The code review is the place to enforce the "human-in-the-loop" principle.
Your Ultimate Safety Net: A Rock-Solid Testing Strategy
If you do only ONE thing from this article, make it this. Testing is your ultimate quality shield against bad AI code.
Specifically, embracing Test-Driven Development (TDD) becomes a game-changer. The TDD workflow fits the AI-assisted model PERFECTLY:
Write the Test First: Before you even open your AI assistant, you write a comprehensive test for the feature you want to build. This test defines what "done" looks like. It is the concrete specification for your feature.
Feed the Test to the AI: You can include the test itself in your prompt! "Here is the Pytest test I've written. Generate the Python code that makes this test pass." This gives the AI an incredibly clear target to aim for.
Generate the Code: Let the AI generate the code to fulfill the test's requirements.
Run the Test: Immediately run the test against the generated code. Does it pass? Great. Does it fail? Now you have a clear, specific list of what's wrong.
Refactor: Refactor the AI's output until the test passes & the code is clean, readable, & efficient. The test suite gives you the confidence to refactor aggressively because you'll know instantly if you break something.
Automated testing at multiple levels (unit, integration, end-to-end) creates a safety net that allows you to leverage the speed of AI without sacrificing quality or stability. It’s the best of both worlds.
Scaling Quality: From Internal Code to Customer-Facing AI
The principles of clean code & maintainability aren't just for your internal codebase. They are JUST as important when you're building AI-powered tools for your customers. Think about it: a messy, unpredictable internal script is a headache. A messy, unpredictable customer-facing chatbot is a business disaster.
This is where the "build vs. buy" or "code vs. no-code" decision becomes really interesting. You could try to code a customer service chatbot from scratch, but you'd run into all the same problems we've discussed. You'd be constantly debugging, refactoring the AI's conversational logic, & struggling to maintain its knowledge base. It can quickly become a "black box" that's impossible to scale or update.
Here's the thing: for a lot of business communication & website engagement tasks, you don't need to reinvent the wheel. Platforms like Arsturn are built to solve this exact problem. Arsturn helps businesses create custom AI chatbots trained specifically on their own data. It provides a structured, no-code platform to build, manage, & maintain a high-quality conversational AI.
Instead of wrestling with the raw output of a generic language model, Arsturn lets you focus on what matters: the knowledge & personality of your chatbot. You upload your website content, PDFs, & other documentation, & it handles the complex parts of creating a reliable AI that can provide instant customer support, answer questions 24/7, & engage with website visitors. For lead generation & boosting conversions, this is HUGE. You're essentially building a highly maintainable & specialized AI without writing a single line of messy code. It's the "clean code" approach to conversational AI, ensuring the bot is a reliable, helpful extension of your brand, not a chaotic mess.
Wrapping It Up
Look, AI code generation is here to stay, & that's a good thing. It has the potential to make us faster, more productive, & free us up from the boring, repetitive parts of coding.
But it’s not magic. It's a tool, & like any powerful tool, it requires skill & discipline to use well. The path to clean, maintainable AI-generated code isn't about finding the perfect AI. It’s about accepting that the AI is just the first step. The real work—the craftsmanship—still lies with you, the developer.
By treating AI as a junior partner, focusing on clean code fundamentals, mastering your prompts, & building a rock-solid testing & review process, you can harness its incredible speed without drowning in technical debt.
Hope this was helpful. Now go build something amazing (and maintainable). Let me know what you think.