METHODOLOGY

Vibe Coding

Building software by prompting an AI tool (Lovable, Cursor, Claude Code, Replit, Bolt, v0) and accepting what it generates without reading it. Fast to a demo, dangerous in production, because the model optimises for code that runs, not code that survives a real user.

Last reviewed: byChristof Jori wiki ↗

Vibe coding is the practice of describing what you want to an AI coding tool and shipping whatever it hands back, without reading the code line by line. The term went mainstream in early 2025, but the workflow predates the name: prompt, run, see it work, prompt again. Tools like Lovable, Cursor, Claude Code, Replit, Bolt, and v0 made it possible for a non-engineer to go from idea to clickable app in an afternoon. That part is real, and it is genuinely useful for prototypes, internal tools, and proving an idea is worth building at all.

The trouble starts the moment a vibe-coded app meets a real user, real money, or real data. An LLM optimises for code that satisfies the prompt and runs in the demo. It does not optimise for the things nobody asked about in the prompt: authorization checks, input validation, secrets management, rate limiting, error handling, the regression suite. Those are exactly the things that separate a demo from production-ready software, and they are exactly the things the model leaves out unless you know to ask, which a non-engineer by definition does not.

Worked example. A founder vibe-codes a SaaS dashboard in a weekend. It has a login screen, a database, a clean UI, and it demos beautifully. What it does not have is a server-side check that user A cannot read user B’s records. The login works, so it feels secure. But every authenticated user can change one ID in the URL and read every other customer’s data. The model was never asked to enforce per-tenant authorization, so it did not, and nothing in the demo surfaced the gap. This is the single most common serious defect in vibe-coded software, and it is invisible until someone, or some regulator, finds it.

The honest trade-off: vibe coding genuinely is faster to a working demo, and pretending otherwise is dishonest. The cost is that speed comes from skipping the parts of engineering that have no visible payoff until they fail. That is a fine trade for a throwaway prototype and a terrible one for anything holding customer data. The mistake is not using the tools. The mistake is mistaking the demo for the product and shipping it. AI-generated code is not inherently worse than hand-written code, but it accrues technical debt silently and skips the same safety work every time.

Wavect’s position is simple: vibe-code the prototype, then run a structured production-readiness pass before real users arrive. That pass is not a vibe. It is the same discipline we apply to any code, TDD on the logic that matters, authorization on every endpoint, secrets out of the client, and a CI/CD pipeline that runs the checks on every change. We do this under Software Quality Assurance. The tools are good. They just do not know what they left out, and neither do you until someone reads it.

// FAQ

FAQs

No, in the right place. It is the fastest way to a working prototype or an internal tool, and for proving an idea is worth building it is excellent. It becomes a problem only when the demo gets promoted to production without anyone reading the security-critical paths. Use it to learn fast, then harden before real users arrive.
Yes, but not as-is. The generated code almost always skips authorization, input validation, secrets management, and error handling, because nothing in the prompt asked for them. Run a production-readiness pass first: read the auth paths, validate every input, get secrets out of the client, add a regression suite. Then it can ship.
Lovable, Cursor, Claude Code, Replit, Bolt, and v0 are the common ones in 2026. They differ in polish, but they share the same blind spot: they generate code that runs, not code that defends itself against a hostile or careless user. That gap is what a QA pass closes.