Vibe Coding

What is vibe coding?

Vibe coding is software development driven mainly by conversation with an AI tool. Instead of writing code line by line, a person describes a feature, a screen or a fix in plain language, the AI generates the code, and the person runs it, checks whether it works and asks for changes. The name describes going with the flow of the AI's output rather than designing each detail upfront.

It relies on a large language model (LLM) trained on large amounts of code, built into editors, chat tools or app builders. Some tools produce full applications from a description; others work inside an existing codebase.

Vibe coding shows up in quick demos, personal tools, internal utilities, hackathons and early product experiments. It lets non-developers build working software and lets developers move faster on routine work. Its limits appear when the software must be secure, maintained or used by many people. That is where the gap between a working demo and a dependable product shows.

What is vibe coding good for?

Vibe coding is strongest where speed matters more than durability, and where mistakes are cheap to find and fix.

• Prototypes: turning an idea into a clickable or working prototype in hours or days.
• Internal utilities: small scripts and tools with few users and low risk.
• Exploration: trying several approaches quickly before choosing one to build properly.
• Learning: helping founders and product people understand what is technically possible.
• Routine code: boilerplate, test scaffolding and simple screens inside a well-structured project, under review.

In each case the value is speed of learning. The output is something to evaluate, not automatically something to ship, and the people using it should know which of the two they are producing.

What are the risks of vibe coding?

The main risk is code nobody understands. If the person accepting changes does not read them, problems pile up unseen: security holes such as exposed keys or missing access checks, duplicated logic, poor error handling and dependencies added without review.

These issues may not show up in a demo. They appear when real users, real data and real attackers arrive. At that point the codebase can be hard to fix, because its structure was never designed, which creates technical debt from the first day.

There are also operational risks: no tests, no readable version history and no clear owner. For anything handling customer data, payments or business-critical workflows, generated code needs the same review as code written by a person.

How do you take a vibe-coded app to production?

Treat the vibe-coded version as a validated prototype and harden it before real users depend on it.

• Review the code: have an experienced developer read the critical paths, especially authentication, data access and payments.
• Add tests: cover the core workflows so future changes do not break them.
• Set up delivery: version control and a CI/CD pipeline so every change is checked before release.
• Fix the structure: separate concerns, remove duplication and replace risky dependencies.
• Plan monitoring: logs, error tracking and backups.

Sometimes rebuilding the core cleanly is faster than repairing it. The prototype still did its job by proving what to build. Either way, the decision should come from the review, not from how finished the app looks on screen.

How is vibe coding different from AI-assisted engineering?

Both use AI to write code. The difference is control. In vibe coding, the person steers by outcome and accepts what works on screen. In AI-assisted engineering, developers set the architecture, write clear specifications, review every change and use tests to check the AI's work.

The second approach keeps much of the speed while keeping the codebase understandable. AI tools draft code, explain unfamiliar areas and handle repetitive changes, but people stay responsible for design decisions and quality.

Many teams use both: vibe coding to explore ideas and build throwaway versions, then disciplined AI-assisted engineering to build the product itself. The skill is knowing which mode a given piece of work needs, and switching deliberately rather than by accident.

Where does vibe coding fit in Vizio AI's work?

In our Venture Studio, AI-assisted development speeds up idea validation and early prototypes, and we use quick, conversational builds where the goal is learning. When a product moves into MVP development, we switch to reviewed, tested software development with a clear architecture.

We also help teams that already have a vibe-coded app. Through product modernization, we review the code, close security gaps, add tests and delivery pipelines, and restructure what needs it so the product can grow safely. The starting point is a short code review that shows whether to harden the current version or rebuild its core.