Is vibe coding ready for production?

Vibe coding lets you describe an application in natural language, ask an AI tool to generate code, run the result, and refine it through more prompts. It can make an idea tangible before you have worked through every implementation detail. A working preview, however, answers only part of the question: does the product do the right thing, and can someone maintain it?

For a founder choosing how to build an MVP, the useful distinction is how the code will be understood, checked, and owned as the product grows.

What does vibe coding mean?

Andrej Karpathy describes vibe coding in his 2025 year in review as creating programs through English-language instructions, with much less attention to the code itself. That makes it different from writing to music, working creatively, or collaborating in a relaxed environment.

The term is also used more broadly for building with AI tools. To make a delivery decision, it helps to ask a more specific question: is someone reviewing the generated implementation, or mainly judging the visible result?

How AI-assisted development changes the workflow

In an engineering workflow, an AI tool might draft a component, suggest a database query, explain an unfamiliar module, or help investigate a failing test. An accountable developer still reviews the changes, checks their fit with the codebase, and verifies the behavior.

GitHub’s guide to reviewing AI-generated code recommends functional checks, review against the project’s intent, dependency scrutiny, and human oversight. Those checks apply whether a change was typed manually, suggested by an assistant, or produced by an agent.

The practical benefit is a shorter path to a draft you can evaluate. The draft still needs to meet the product’s requirements.

Where a prototype can help

A prototype is useful when the main uncertainty is about the experience: which information belongs on a screen, whether a navigation flow makes sense, or what someone needs before taking the next step. Small experiments using sample data can help a team compare those choices.

Consider a hypothetical property-search prototype. A prompt could describe listing cards, location filters, a budget range, and a favourites button. Trying that interface may reveal that people need different filters for a full apartment and a shared room.

That experiment does not establish that real search results are accurate, saved listings belong to the right account, or private messages are protected. Those are separate requirements with separate checks.

For a concrete example of product scope and integrations, see our FindMyFlat case study. It describes the application and its stack; the prototype above is an illustration, not a claim about how FindMyFlat was developed.

What needs checking before launch?

Start with the actions and data the application handles. For a public product, the review should cover:

• Expected behavior: confirm important flows, error states, empty results, and recovery paths against agreed requirements.
• Account permissions: check who can read, create, edit, and delete each kind of record, including requests made outside the visible interface.
• Server-side validation: enforce rules where the data is processed, rather than relying on form fields or hidden buttons.
• Secrets and integrations: keep private credentials on the server and review third-party dependencies and API access.
• Release and maintenance: establish deployment, rollback, monitoring, and who will investigate problems after launch.

These are engineering responsibilities for the actual application. A successful demo or an AI-generated explanation does not verify them. Lovable’s security guidance, for example, explains that browser code can be inspected or bypassed and that sensitive checks belong on the backend.

Choosing an approach for your product

For a small experiment with sample data, a prompt-driven prototype may be enough to test an idea. As soon as people rely on the application, agree on the implementation review, testing, data access, and maintenance it needs.

For an existing product, start with a bounded change and review how it interacts with authentication, integrations, and the established architecture. Avoid accepting a large generated rewrite simply because its preview looks complete.

At Formics, we scope web application development, mobile products, and AI-agent workflows around the job the software needs to do. If you already have a prototype, share it with us along with the decisions you have tested and the parts that still need engineering review.