Safe Vibing: A Guide to Using AI Code Generators in Production
Z
Zack Saadioui
8/11/2025
So, you’ve probably heard the term "vibe coding" floating around. It's been buzzing on X (formerly Twitter), popping up in tech blogs, & making the rounds at developer conferences. If you haven't, here's the gist: vibe coding is this new way of building software where you're less of a line-by-line code writer & more of a creative director for an AI. You describe what you want in plain English, the AI pumps out the code, you test it, & you iterate based on the "vibe" until it feels right. It's fast, it's creative, & honestly, it's a little bit wild.
This whole trend was kicked into high gear by a tweet from the legendary Andrej Karpathy back in early 2025, & it’s taken the dev community by storm. The idea is to "fully give in to the vibes, embracing exponentials, & forgetting that the code even exists." It's a powerful thought, & it's making software development more accessible to everyone from indie makers to non-programmers.
One of the most popular tools at the heart of this movement is Cursor, the AI-first code editor. It’s a fork of VS Code, so it feels familiar, but it's packed with AI features designed for this new way of working. You can chat with it about your entire codebase, have it generate code with a simple
1
Ctrl+K
, let it fix its own errors, & even reference web pages or documentation directly in the chat. It’s pretty magical stuff & feels less like a tool & more like a pair programmer.
But here’s the thing. While vibe coding is AMAZING for prototyping, spinning up MVPs, or just messing around with new ideas, what happens when the stakes are higher? What happens when you're working in a production environment? Can you really "just vibe it" when you have real users, real data, & real security concerns?
That's the million-dollar question, isn't it? It’s one thing to build a cool side project with AI, but it's a whole other ball game when you’re pushing code to production. The speed is seductive, but the risks are real. This is where we need to move from pure "vibe coding" to something a bit more… structured. Let's call it "safe vibing."
In this article, we're going to dive deep into how to use a tool like Cursor in a professional, production setting without setting everything on fire. We’ll talk about the very real dangers you need to be aware of & then get into the practical tips & tricks to use these powerful AI tools safely & effectively.
The Dark Side of the Vibe: Real Risks of AI Code in Production
Look, I get it. Watching an AI spin up an entire REST API from a single prompt feels like a superpower. But with great power comes great responsibility, & all that. When you're moving at the speed of AI, you can easily bypass the traditional gates that keep our production environments safe & sound—things like thorough code reviews, architectural discussions, & methodical testing. Here are the biggest risks you need to have on your radar.
1. Security Vulnerabilities: The Trojan Horse in Your Code
This is the big one. AI models are trained on massive datasets of public code from places like GitHub. The problem? A lot of that public code is, frankly, not great. A Stanford University study found that developers using AI coding assistants were more likely to produce insecure code.
Here’s why:
Training on Flawed Code: The AI can unconsciously reproduce security vulnerabilities it learned from its training data. Think outdated practices, common exploits, or just plain bad habits.
Outdated Dependencies: The AI’s knowledge is frozen at the time of its last training. It might suggest using a library or dependency that has a known vulnerability that was discovered after its training cutoff.
Superficial Security: AI-generated code often looks secure on the surface but can contain subtle flaws in things like authorization, data handling, or input validation that only surface under specific conditions in a production environment. For example, it might not grasp the specific data security regulations like HIPAA for a healthcare app you're building.
2. Code Quality & Technical Debt: The "It Works, But..." Problem
AI is great at generating code that works. But "working" isn't the same as "good." The code can often be inefficient, hard to read, & a nightmare to maintain. This is how technical debt quietly creeps into your codebase.
Common issues include:
Non-Optimal Solutions: The AI might give you a brute-force solution that functions but doesn't scale or perform well under load.
Ignoring Conventions: The generated code probably won't follow your project's specific conventions, style guides, or architectural patterns unless you're VERY explicit. This leads to an inconsistent & messy codebase.
"Spaghetti Code": When developers rely too heavily on auto-generated solutions without fully understanding them, it can lead to disorganized & unmanageable code that’s nearly impossible to debug or modify later.
3. The "Black Box" Dilemma & Atrophied Skills
Another major risk is the gradual erosion of your own engineering skills. When you're just prompting & accepting, you’re not exercising your problem-solving muscles. You lose a deep understanding of the codebase.
This creates a few problems:
Debugging Becomes a Nightmare: If you didn't write the code & don't fully understand its logic, how are you supposed to fix it when it breaks at 3 AM?
Dependency Lock-In: You become so reliant on the AI that you struggle to solve problems without it. Your ability to think critically & independently starts to fade.
Knowledge Gaps: Junior developers might not learn the fundamentals properly, & senior developers might find it hard to mentor when their own process is heavily abstracted by AI.
4. Intellectual Property & Licensing Risks
This is a legal minefield that many people overlook. AI models are trained on vast amounts of code, including projects with specific open-source licenses. The AI usually doesn't tell you the source or the license of the code it generates. This means you could unknowingly include copyrighted or restrictively licensed code in your proprietary, commercial product, leading to serious legal trouble down the line.
Safe Vibing: How to Use Cursor in Production Like a Pro
Okay, so the risks are scary. But that doesn't mean we should throw our AI assistants out the window. The productivity gains are too massive to ignore. The solution isn't to stop using these tools; it's to use them smarter. Here are the tips & tricks for using Cursor safely in a production environment.
1. Master the Context: Garbage In, Garbage Out
The single most important factor for getting good, safe output from Cursor is providing it with the right context. The AI isn't magic; it can only work with what you give it.
Here’s how to become a context master:
Use the
1
@
Symbol Religiously: This is your best friend in Cursor. You can use
1
@
to reference specific files, folders, or even web pages & documentation. Instead of a generic prompt, say something like, "Using the patterns in
1
@/lib/auth.ts
, create a new service in
1
@/services/userService.ts
that..."
Curate Your Tabs: Cursor uses your open files as part of its context. Keep your workspace clean. Close irrelevant files & only keep open what's directly related to the task at hand. You can quickly add all open editors to the context with
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/Reference Open Editors
.
The Power of
1
.cursorrules
&
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.cursorignore
: These files are your secret weapons.
1
.cursorrules
: This is a project-specific file where you can give the AI high-level instructions that it will always consider. For example: "Always use strict types in TypeScript. All API endpoints must have Joi validation. Write tests using Jest."
1
.cursorignore
: Just like
1
.gitignore
, this file tells Cursor which files & folders to ignore when indexing your codebase, keeping the context clean & focused.
Keep Your Index Fresh: If you're making lots of changes, your code index can get stale. If the AI is referencing old files or giving weird suggestions, manually resync the index in the settings.
2. The Edit-Test Loop: Your Safety Net
Never, EVER, blindly accept AI-generated code & move on. The "Edit-Test Loop" is a powerful strategy that combines the speed of AI with the rigor of Test-Driven Development (TDD).
The workflow looks like this:
Define a small, focused task.
Write a failing test case for that task. You can even have the AI help you write the test!
Instruct Cursor to write the code that makes the test pass. Be specific in your prompt.
Run the test.
If it fails, don't fix it yourself! Feed the error message back to Cursor & tell it to fix its own mistake. This is where Cursor's "Loops on Errors" feature shines.
Review the code once the test passes. Now you're not just checking if it "works," you're reviewing it for style, efficiency, & potential edge cases.
Commit your changes. Small, frequent commits are key.
This process forces you to think about requirements upfront & provides an automated safety net to catch regressions & bugs before they hit production.
3. Build a "Second Brain" for Your Project
AI assistants have context limits. Once you've been chatting for a while, they start to forget the beginning of the conversation. To combat this, you can create a "second brain" for your project using simple markdown files.
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technical.md
: An overview of your project's architecture, tech stack, & key libraries.
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tasks.md
: A breakdown of the current feature you're working on, including requirements & acceptance criteria.
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status.md
: A running log of what's been done, what issues have been encountered, & what's next.
When the AI starts to get confused, you can just
1
@
these files to quickly restore its context without re-typing everything. It’s a simple but incredibly effective technique.
4. Integrate Automated Security Scanning
Since AI can bypass traditional security checkpoints, you need to make security testing as automated as the code generation itself. This is where tools like StackHawk come in. You can integrate runtime security testing directly into your workflow.
You can even create rules in your
1
.cursorrules
file to remind the AI (and yourself) to run security scans. For example, you can set up a rule that says, "Before committing, validate the configuration with
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hawk validate
." By configuring the scanner to output results in a machine-readable format like SARIF, you can feed the vulnerability report directly back to Cursor & ask it to fix the security holes it created. This closes the loop & makes security a continuous part of your development process, not an afterthought.
5. Create Custom Modes for Your Workflow
Cursor's default "Ask" & "Agent" modes are great, but they are generic. For production work, you can create custom modes that are tailored to your specific needs. A custom mode is like creating a specialized AI persona that already knows your context.
You can define things like:
Your Role: "You are a senior backend engineer working on a NestJS application."
Your Stack & Patterns: "We use TypeORM for the database layer & follow a modular microservices architecture."
Your Team's Standards: "Always generate documentation in the TSDoc format for exported functions."
By creating custom modes for different tasks (e.g., "Feature-Builder," "Bug-Fixer," "Security-Auditor"), you give the AI a powerful head start & get much more relevant & safer code suggestions.
A Note on AI Chatbots & Customer Interaction
It's worth mentioning that while we're talking about using AI for code generation, the same principles of safety & context apply when using AI for other business functions. For instance, many companies are now deploying AI chatbots for customer service. If you're building a feature that involves customer interaction, like a support widget on your website, you need to ensure the AI behind it is just as robust & well-trained.
This is where a platform like Arsturn can be incredibly valuable. While Cursor helps you build the software, Arsturn helps you build the AI that interacts with your users. You can create custom AI chatbots trained on your own data—your documentation, your knowledge base, your past customer conversations. This ensures the chatbot gives accurate, context-aware answers & provides instant, 24/7 support. By using a no-code platform like Arsturn, you can build a sophisticated, personalized customer experience without your engineering team having to manage the complexities of a production-level conversational AI, allowing them to focus on the core product.
Conclusion: You're Still the Pilot
Vibe coding is more than just a buzzword; it represents a fundamental shift in how we build software. Tools like Cursor are making developers more productive than ever before, but they don't replace the need for critical thinking, careful oversight, & solid engineering principles.
The key takeaway here is that you are still the pilot. The AI is an incredibly powerful co-pilot, but you are the one who is ultimately responsible for the code that gets shipped to production. By mastering context, embracing a test-driven workflow, integrating automated security, & customizing your tools, you can harness the incredible speed & creativity of vibe coding without compromising on the quality, security, & maintainability that professional software demands.
So go ahead, vibe it. But vibe it safely.
Hope this was helpful! Let me know what you think.