Vibe Coding: Building Software Through AI Dialogue

Vibe coding in one sentence

Vibe coding is building software in dialogue with an AI: you describe in plain language what should be built, the model writes the code, you look at the result and describe the next change — without reading or understanding the code yourself.

The last part of that sentence is the crucial one. Using AI while programming has been everyday practice for years. What sets vibe coding apart is the deliberate decision to give up control over the result. You judge whether it works, not how it's built.

The term had a remarkable run in 2025: coined half-jokingly by Andrej Karpathy in a post in February 2025, it was named Word of the Year by the Collins Dictionary that very same year. In between lies the usual hype cycle, along with the disillusionment that typically follows.

What's new here — and what isn't

The bottom row is the crux of the problem. As long as the AI is fixing bugs you can describe, you keep moving forward. The moment a bug comes up that you can't describe — because it's buried deep in the structure or only happens sometimes — you're stuck. Then the model changes things on request that used to work, and the project starts to oscillate.

The three tool categories

Editor assistants. Development environments with AI deeply built in, where you change entire files or project areas through chat. The best-known example is Cursor. The target audience is developers who want to work faster — here the code stays visible, so review is still possible.

Terminal and agent tools. Programs that work independently within the project: reading files, making changes across multiple files, running tests, fixing bugs on their own. Claude Code is the most prominent example here. These tools are genuine AI agents with everything that entails — including the need to limit permissions and approve actions. Via the Model Context Protocol they also connect to documentation, ticketing systems, or databases.

App builders. Platforms like Lovable, v0, or Replit, where a description turns into a working web application complete with interface, database, and publishing. This is the category non-developers mean when they talk about vibe coding — and the one with the biggest gap between demo and everyday reality.

What it's really good for

There's a clear zone where the method doesn't just work, but is clearly superior:

• Prototypes and feasibility checks. An idea that used to take two weeks to reach a first look is ready in an afternoon. For the question “is this even worth pursuing?” that's real progress.
• Internal tools with a small user base. A form, a reporting dashboard, a converter for three colleagues. Manageable risk, clear benefit.
• One-off scripts. Data conversion, bulk renaming, evaluating a spreadsheet. Runs once, never touched again afterward.
• Learning and understanding. Trying out how something works without first slogging through the setup.

The pattern behind it: few users, no sensitive data, short lifespan, visible errors. If all four apply, vibe coding is the fastest option there has ever been.

Where it breaks down

The last-thirty-percent problem. The first seventy percent of an application comes together astonishingly fast. The rest — edge cases, error messages, permission management, behavior under load — then takes just as long as it always has. Except now you have to find your way around a codebase you didn't write.

Security is the sore spot. Generated code regularly contains the classic gaps: open database access, missing permission checks, access keys left in the source code, forms without input validation. Not because the models are incapable, but because nobody asked for it — and if you don't read the code, you don't see it. Once real user data is involved, unreviewed code isn't a shortcut, it's a liability risk.

Maintenance without understanding. Six months from now, something needs to change. That old AI session is gone, the model is a different one, and nobody can explain why the application was built the way it was. The usual outcome: rebuild instead of adjust.

The responsibility fog. When it's not clear who's responsible for the code, nobody is. That's less a technical problem than an organizational one — and in companies, the more common one.

Rules for production use

Once AI-generated code moves beyond the prototype stage, it needs guardrails. Five that have proven themselves:

1. Classify the intended use up front. Internal experiment, internal tool, or something customer-facing? From the third tier onward, a human subject-matter review is mandatory, no exceptions.
2. No real data in the build process. Neither customer data nor production credentials. Test data costs an hour and saves you from the real thing going wrong.
3. Small steps, constant review. One change, test, save. Anyone who describes five wishes at once gets a result that's different from what they had in mind in three places — and no longer knows where.
4. Version control from day one. Yesterday's state has to be recoverable. This is the single most important measure of all.
5. Describe precisely. Context, constraints, and success criteria belong in the brief. The principles from Prompt Engineering: Fundamentals & Techniques apply here one to one — “build me a shop” reliably leads nowhere.

What this means for non-developers

The honest answer: you'll get further than ever before, but not as far as the demos suggest.

What's realistically achievable: internal tools, prototypes, automations, small applications without sensitive data. That's a lot — and for many tasks that used to require a full development project, it's now completely sufficient.

What's not realistic: a customer-facing application with payment data, login, and operational responsibility with zero development experience behind it. Not because the tools technically prevent it — they don't — but because you're missing the questions you need to ask before something like that goes live.

For many operational tasks, an automation platform is the better path anyway, compared with building your own application: visible workflows, ready-made integrations, no code to maintain. The comparison in Creating AI Agents weighs the different build paths against each other.

Conclusion

Vibe coding is a genuine shift, just not the one the hype sold. It dramatically lowers the barrier to entry for a first draft — and leaves the requirements for everything that comes after unchanged: security, maintainability, accountability. Use it for prototypes, internal tools, and one-off scripts without hesitation. And treat any code that touches real user data for what it is: a draft that a human has to review before it goes live.