Vibe coding can turn an idea into something visible with remarkable speed. Describe the behaviour, accept the suggested code, adjust the prompt and repeat. For prototypes, internal experiments and disposable tools, that is genuinely useful.
The trouble begins when a convincing demonstration is mistaken for a dependable system. Software can appear complete while important decisions about data, security, failure, maintainability and ownership remain unresolved. AI can accelerate implementation, but it does not remove the engineering work. It often hides how much of that work is still outstanding.
A working screen is not a working system
Most software failures do not come from an inability to produce code. They come from misunderstood requirements, untested assumptions and interactions nobody considered. A generated feature may perform perfectly on the happy path while behaving unpredictably with incomplete data, concurrent users, interrupted requests or changing dependencies.
A prototype answers a narrow question: can this interaction be made to work? Production software has to answer a much larger set of questions. What happens when it does not work? Which data is authoritative? Who is allowed to take an action? Can a mistake be reversed? How will anyone know that the system is degrading?
The gap is not between human-written code and AI-written code. It is between code that has been produced and a system that has been engineered.
The model is still making design decisions
Prompting can feel like avoiding technical decisions. In reality, the decisions are still being made—often implicitly, one generated change at a time. The model chooses libraries, data structures, trust boundaries and error-handling patterns unless someone gives it constraints.
Those choices may be locally reasonable but globally inconsistent. One feature validates data in the browser, another trusts the server request. One route handles authorisation centrally, another checks a user identifier directly. Each answer looks plausible in isolation. The system that emerges from them may have no coherent architecture.
Experienced guidance matters because it supplies context the prompt rarely contains: which decisions must be consistent, where change is likely, what the organisation can operate and which shortcuts create unacceptable risk.
Fast generation can create slow uncertainty
When nobody fully understands the generated code, every later change becomes an investigation. Teams become reluctant to modify apparently fragile areas. Similar functionality is added in parallel because the existing path is difficult to reason about. Fixes address symptoms without clarifying the underlying behaviour.
This is not an argument for manually typing every line. It is an argument for retaining ownership of the system. Someone must be able to explain why the design is appropriate, identify its failure modes and decide whether a proposed change preserves the important guarantees.
Guardrails turn speed into leverage
AI-assisted development becomes far more useful when it operates inside explicit boundaries. The guardrails do not need to be elaborate, but they need to exist before generated code starts defining the system by accident.
- Defined behaviour. State the business rules, edge cases and acceptance criteria before asking for implementation.
- Architectural boundaries. Decide where data, validation, authorisation and external integrations belong, then keep those decisions consistent.
- Automated verification. Use tests, type checks and repeatable builds to challenge generated output rather than relying on a successful demonstration.
- Security review. Treat identity, permissions, secrets, personal data and third-party input as design concerns, not a final prompt for “best practices”.
- Observable operation. Define what should be logged, measured and alerted on so failures are visible outside a developer’s browser.
- Human review. Require someone with the necessary experience to understand material changes and accept responsibility for them.
Where vibe coding works well
Used deliberately, the approach is valuable. It can shorten the distance between an idea and a testable prototype. It can automate small, bounded tasks; explore interface alternatives; create migration scripts that will be reviewed; or help an experienced engineer move faster through familiar territory.
The common factor is not the size of the codebase. It is the clarity of the boundary. The lower the consequence of failure and the easier the output is to verify, the more freedom there is to work by feel. As data sensitivity, operational dependence or integration complexity increases, the need for engineering discipline increases with it.
The useful question is not whether AI wrote it
The useful question is whether the result can be trusted. Can the team explain its behaviour? Are important assumptions tested? Are access and data flows deliberate? Can failures be detected and recovered from? Can another engineer change it without starting again?
AI changes the economics of producing software. It does not change the qualities that make software dependable. Experience, guidance and guardrails are what convert rapid generation from an impressive demo into a system that can carry real work.