Getting Started with Vibe Coding: 10 Mistakes to Avoid in Your AI Projects

Describe an idea, wait a few minutes, and suddenly a working web app appears in front of you. What sounded like science fiction just a few years ago is everyday reality with modern AI coding tools.

ChatGPT, Codex, Claude Code, GitHub Copilot, Cursor, and other coding agents can write functions, create files, analyze existing projects, hunt down bugs, and sometimes even run tests or terminal commands on their own.

Welcome to vibe coding.

But that's exactly where the danger lies: just because an application works doesn't mean it was built well.

Vibe coding can massively accelerate your development. But if you start without structure, accept every AI suggestion unchecked, and give a coding agent unrestricted access to your system, you can end up within a few hours with a project that's barely maintainable, insecure, or simply impossible to understand.

This article covers the ten most common vibe coding mistakes — and, more importantly, how to avoid them.

What exactly is vibe coding?

The term vibe coding was popularized in early 2025 by computer scientist Andrej Karpathy. It describes a form of software development where developers mainly describe in natural language what they want to achieve, while the language model generates most of the actual code.

IBM now frames vibe coding as a shift toward more intent-driven software development: humans increasingly describe the desired outcome, while AI systems help with the technical implementation — though the developer still stays responsible for specification, architectural review, and quality control.

The difference from classic AI autocomplete is substantial.

A modern coding agent no longer just suggests the next line of code. Depending on the tool and its configuration, it can edit entire files, install dependencies, run shell commands, kick off tests, and carry out several steps of a development process on its own. The OWASP Top 10 for Agentic Applications now explicitly flags additional security risks in agentic coding tools, including identity and privilege abuse, tool misuse, and unexpected code execution.

That's exactly why our own working habits need to change too.

Vibe coding isn't the problem — missing control is

It would be wrong to write off vibe coding as bad software development in general.

Used well, it's an excellent tool.

A developer might say something like:

"Add a new REST endpoint for suppliers to my existing Flask app. Use the existing SQLAlchemy structure, add input validation, and write unit tests for both successful and failing requests."

A good coding agent can turn that into a surprisingly usable first draft within a short time.

Things get problematic with prompts like:

"Build me a complete inventory management system with React, Python, MySQL, login, invoicing, and user management."

A modern AI can produce a lot of code from that too.

But afterward, nobody reliably knows which architectural decisions the AI made, which dependencies it pulled in, which security mechanisms are missing, and which features only happen to work in the current test run.

So the most important rule is:

Use AI to speed up your software development — not to switch off your own technical judgment.

Mistake 1: You start without a clear project scope

Perhaps the most common vibe coding mistake happens before the AI has even created its first file.

The project idea is too big.

What starts as:

"I need a small app to manage my servers."

turns, within a few prompts, into:

"With user management, monitoring, SSH connections, a dashboard, notifications, AI analysis, automatic repairs, and a mobile interface."

This problem is called scope creep. AI actually amplifies it, because new features seem to cost nothing. Another button is quick to generate. So is another database model.

But every additional feature increases the complexity of the whole system.

Better: define a minimum viable product

Before you write the first prompt, decide what version 1 actually needs to do.

Instead of a full server management platform, the goal could be, for example:

Version 1 should be able to store, edit, and delete servers. Each server has a name, IP address, operating system, and description.

Nothing more.

Once that feature is stable, the next one follows.

That way, your project stays understandable even when a large part of the code is generated by AI.

Mistake 2: You let the AI build the whole project in one pass

The classic vibe coding prompt reads roughly like this:

"Build me a complete application."

The impressive result might end up being 30 files, several thousand lines of code, and five frameworks.

And that's exactly where the problem starts.

When something breaks, you barely know where to start looking.

Incremental development works much better.

First the project structure. Then the database. Then a single API function. Then the frontend for it. Then tests.

A sensible instruction to a coding agent might be:

"First analyze the existing project. Don't change any code yet. Explain which files would be affected by the new user management feature, and propose an implementation plan with a maximum of five steps."

Only after that does the actual implementation begin.

That sounds slower.

In larger projects, it's almost always faster.

Mistake 3: You accept AI-generated code without question

The app starts up.

The button works.

The data lands in the database.

So everything's fine?

Unfortunately, no.

AI-generated code can be factually wrong, needlessly complicated, insecure, or simply outdated. The most dangerous bugs are the ones that don't show up during a quick surface-level test.

Examples include missing input validation, unsafe SQL queries, race conditions, incorrect error handling, or permission checks that only exist in the frontend.

After every significant change, you should be asking:

Why does this code work?

Have the AI explain critical sections to you. Even better: have it run a second pass as a reviewer.

For example:

"Review the function you just implemented like a senior code reviewer. Look for security issues, missing error handling, unnecessary complexity, and possible side effects. Don't change anything yet."

Vibe coding works much better when you alternate between using the AI as a developer and as a reviewer.

Mistake 4: You work without Git and restore points

This mistake can destroy an entire project.

You have a working version.

Then you tell the coding agent:

"Clean up the code and improve the architecture."

Five minutes later, 17 files have changed, two components are gone, and the app no longer starts.

Welcome to vibe coding without version control.

Git should be set up before the very first line of AI-generated code.

Once you reach a stable checkpoint, commit it.

For example:

git commit -m "User management basic version works"

Only then should the AI keep going.

If something breaks, you can always roll back to a working state.

For production repositories, a protected main branch is also worth setting up. GitHub supports requiring pull requests, reviews, and status checks before changes can land on important branches. For a closer look at how Git, sandboxing, backups, and approvals fit together for a specific coding agent, see How to Use OpenAI Codex Safely: Git, Sandbox, Backups, and Approvals (Read article).

In vibe coding, your Git repository isn't just version history. It's your safety net.

Mistake 5: You only start testing once the project is "done"

One of the most dangerous phrases in AI-assisted coding is:

"We'll test that later."

Later usually means: once so much code depends on other code that nobody can tell anymore where the bug actually came from.

Tests should therefore be part of every single development step.

When an API function is created, tests for it should be created at the same time. When a permission check is added, you need to verify that an authorized user gets access — and an unauthorized one doesn't.

Modern coding agents can already run some of these checks on their own, including project tests and linters.

But the same rule still applies:

A green test only means something if the test itself is meaningful.

Don't just have code generated for you.

Have it explained what's being tested and which cases are still missing.

Mistake 6: You hand API keys and passwords directly to the AI

"Here's my API key. Please wire it into the app."

Please don't.

Credentials don't belong in prompts, and they don't belong hardcoded in source code either.

That includes API keys, database passwords, SSH keys, cloud tokens, JWT secrets, and credentials for external services.

Use environment variables or a proper secret store instead.

A typical application, for example, reads:

DATABASE_PASSWORD

from the environment, without the password itself ever being stored in the repository.

It's also worth remembering: a .gitignore doesn't automatically protect you from an AI agent. It mainly stops files from being tracked by Git. An agent with filesystem access can still read an existing .env file.

OWASP specifically recommends excluding sensitive files such as .env, private keys, and credential files from an AI tool's context, and keeping secrets out of the project directory entirely. For a closer look at using .env, Vault, and permissions the right way for AI agents, see Protecting Secrets and API Keys in AI Agents: .env, Vault, and Permissions (Read article).

Mistake 7: Your coding agent gets far too many permissions

Modern coding agents keep getting more capable.

And with that, more dangerous.

An agent with terminal access can potentially install packages, delete files, run Git commands, open network connections, or call external tools.

On your personal test machine, that might be acceptable.

On a production server, it's an entirely different situation.

So work by the principle of least privilege.

The agent gets only the permissions it actually needs. For a deeper look at how to scope agent permissions sensibly, see What Permissions Should an AI Agent Get? Security Rules for Agents (Read article).

For local experiments, a virtual machine, a container, or another isolated development environment is a much better fit than an agent with unrestricted access to your entire workstation, let alone your production infrastructure.

Be especially careful with automatic approval modes that let the agent run commands without asking first.

Speed is nice.

An unsupervised rm, database migration script, or deployment can get expensive very quickly.

Mistake 8: You let the AI install any dependency it wants

Coding agents love libraries.

A small problem quickly gets solved with an extra npm package. Then another one. Then another.

At first, the result works beautifully.

Six months later, your project depends on dozens of direct and hundreds of indirect dependencies.

Every additional dependency means extra maintenance work and potentially extra security risk.

So before adding a new library, ask:

Do we actually need this dependency?

A good prompt might be:

"Before installing a new dependency, first check whether this can be implemented with libraries we already have, or with the standard library. New dependencies must be justified before installation."

Serious projects should also include automated dependency and vulnerability scanning.

AI can help you build software faster.

It shouldn't quietly turn your project into a dependency graveyard along the way.

Mistake 9: You ignore architecture and documentation

Vibe coding often starts out feeling incredibly pleasant.

You describe a feature.

The AI implements it.

You describe the next one.

The AI implements that too.

After a few weeks, though, nobody remembers the original architecture anymore.

One component uses services. Another talks directly to the database. A third has its own helper class. Somewhere along the way, three different functions now do almost the same thing.

This happens especially often when the coding agent only sees part of the project in each new session.

That's why even an AI-generated project needs clear rules.

Write down, for example, which technologies are used, how the project is structured, where business logic lives, what naming conventions apply, and how tests are organized.

Depending on the tool, such rules can also live in project-specific instruction files — AGENTS.md for Codex, or CLAUDE.md for Claude Code. For which file fits which tool, and how to combine both without maintaining duplicate rules, see AGENTS.md vs. CLAUDE.md: Which File Does Your Project Need? (Read article).

A good README matters at least as much.

It should let a human understand:

What does the project do? How do you start it? What components exist? What dependencies does it need? How do you run the tests? How does deployment work?

If only your AI agent understands the project, you don't have a maintainable piece of software.

You've created a dependency on your AI agent instead.

Mistake 10: You treat a working prototype like finished production software

This is probably the most important difference between successful and dangerous vibe coding.

A prototype is allowed to be improvised.

Production software isn't.

Just because your app works locally doesn't mean it belongs on the public internet.

Before a production deployment, you should review authentication, authorization, input validation, error handling, logging, backups, updates, dependencies, secrets, database migrations, and recovery options.

Applications that integrate AI or AI agents themselves come with extra attack surface on top of that.

OWASP still lists prompt injection as one of the central risks for applications built on large language models. Manipulated input can change a model's behavior and, through connected tools, trigger unwanted actions in the worst case.

So the rule is:

Prototype first. Harden second. Production third.

Not:

Prototype first. Deploy immediately.

What a good vibe coding workflow looks like

A controlled vibe coding process doesn't have to be complicated.

Start by defining one small, clearly described feature. Have the existing code analyzed before any changes are made. Ask for a short implementation plan, and then implement only a manageable slice of it.

After that, run the tests, review the result, and save a working checkpoint in Git.

Only then move on to the next feature.

Security-relevant changes get an extra review step on top.

A workflow like this feels more formal than a spontaneous "just do it."

In practice, though, it's exactly what prevents that all-too-common situation where, three hours into AI-generated changes, nobody knows anymore why the app stopped working.

A good starting prompt for your first vibe coding project

Instead of simply writing:

"Build me an application."

you can set your coding agent up for success with something like this:

We're building this project step by step. First analyze the
existing structure and don't change any files yet. Use existing
technologies and libraries before suggesting new dependencies.
Create a short implementation plan for every task first. Changes
should stay small and easy to follow. Existing functionality must
not be removed without an explicit reason. After every
implementation, run the existing tests and linters. Security-
relevant changes must be reviewed separately afterward. Secrets
must never be written into code or configuration files.

Rules like these alone change the quality of many AI coding sessions dramatically.

The AI doesn't just get a goal.

It gets a working process.

When is vibe coding especially well suited?

Vibe coding is excellent when you want to build prototypes quickly, create internal tools, generate repetitive boilerplate, or extend an existing application.

Experienced developers benefit enormously from it too.

The real advantage isn't that you no longer need to understand programming.

The bigger advantage is spending less time on routine work.

You can focus your attention on architecture, business logic, user experience, and security decisions while the AI handles many technical details faster. Which specific tool fits best depends heavily on the project — a direct comparison can help: Claude Code vs. OpenAI Codex: Which Coding Agent Is Better? (Read article).

The more critical an application becomes, though, the more important human oversight gets.

A small personal to-do app needs different security measures than a public customer portal handling personal data.

Vibe coding isn't a one-size-fits-all development process.

How much control you need always depends on the project's risk.

Vibe coding for beginners: do I still need to know how to code?

Not necessarily, to get started.

But very likely, to build good software in the long run.

AI lowers the barrier to entry enormously. People without a formal development background can now build working applications that would have required far more technical knowledge in the past.

That's a fantastic development.

The problem only shows up when "I can generate software" gets confused with "I can judge software."

You don't have to be able to write every line yourself.

But you should increasingly understand how your application is structured, where data is stored, how users are authenticated, which services talk to each other, and what the consequences of a change can be.

Vibe coding can actually help with that too.

Don't just use the AI as a programmer.

Use it as a teacher as well.

Conclusion: successful vibe coding needs rules

Vibe coding is fundamentally changing software development.

Ideas can be tried out faster. Prototypes appear within hours. Routine work can be automated, and even small teams can ship projects that would have required far more development time in the past.

But AI's speed doesn't remove the classic requirements for good software.

Git still matters.

Tests still matter.

Security still matters.

Backups still matter.

Code reviews still matter.

And above all, one thing still matters: responsibility.

The coding agent can generate code.

Whether that code is good enough, secure enough, and understandable enough for production is still your call.

So if you take away just one rule from this article, make it this one:

Vibe code fast — but stay deliberately in control.

Then AI won't become a risk to your project. It'll be the tool it was always meant to be: an enormously powerful accelerator.

Frequently asked questions about vibe coding

What does vibe coding mean?

Vibe coding describes a form of AI-assisted software development where developers mainly describe in natural language what features or changes they need. An AI model or coding agent then generates large parts of the actual code.

Is vibe coding only suitable for beginners?

No. Experienced developers can benefit enormously too, since they can delegate routine work to AI and focus more on architecture, business logic, reviews, and complex problems.

Is AI-generated code safe?

Not automatically. AI-generated code should be tested, reviewed, and secured just like manually written code. Authentication, permissions, input validation, secrets, and external dependencies deserve particular attention.

Which tools are good for vibe coding?

Numerous tools now exist for this, including ChatGPT or Codex, Claude Code, GitHub Copilot, Cursor, and other AI coding agents. Which tool fits best depends on project size, the desired degree of automation, the development environment, and security requirements.

Do I need Git for vibe coding?

Yes. Because coding agents can change a large number of files in a very short time, version control matters even more than usual. Small commits and working restore points let you quickly undo problematic AI changes.

Can I build complete applications with vibe coding?

Yes. Modern AI coding tools can already produce surprisingly extensive applications. The larger the project gets, though, the more important clear architecture rules, tests, version control, reviews, and security measures become.

Should a coding agent get direct access to production servers?

In most cases this should be avoided, or at least very tightly restricted. Coding agents should only receive the permissions they actually need, following the least-privilege principle. Development, test, and production environments should be kept cleanly separated.

Sources and fact-check

The definition and framing of vibe coding draws on IBM's current 2026 perspective. Recommendations on AI coding security, agent risk, and prompt injection are based on the OWASP Top 10 for Agentic Applications 2026 and current OWASP guidance on secret handling. Information on branch protection, pull request reviews, and status checks follows the current GitHub documentation.

Google continues to recommend helpful, reliable, people-first content over text produced mainly for search engines. This article is deliberately built around concrete problem-solving, practical and verifiable tips, and a complete answer to the underlying search intent.

Technical review as of: September 18, 2026.

Related topics

How to Use OpenAI Codex Safely: Git, Sandbox, Backups, and Approvals (Read article)

Protecting Secrets and API Keys in AI Agents: .env, Vault, and Permissions (Read article)

What Permissions Should an AI Agent Get? Security Rules for Agents (Read article)

AGENTS.md vs. CLAUDE.md: Which File Does Your Project Need? (Read article)

Claude Code vs. OpenAI Codex: Which Coding Agent Is Better? (Read article)

Role-Based Access Control for AI Agents: How to Get RBAC Right (Read article)

How to Operate MCP Servers Securely: Permissions, Tools, and Risks Explained (Read article)

Codex App vs. Codex CLI vs. IDE: Which One Fits Your Workflow? (Read article)