Is Vibecoding a Good Start? Six Days, Four Prototypes, and One Rule That Made It Safe
A media group’s innovation team wanted to know whether AI-generated code ("vibecoding") was a legitimate way to start a project or a shortcut that creates debt. Envion ran a six-day validation sprint producing four working prototypes, on one non-negotiable condition: none of the generated code would reach production. Two concepts were killed after user testing, one was parked, and one was rebuilt properly in five weeks. The answer is yes — as a way to decide, never as a way to ship.
The challenge
The question was not "can AI write code" — the team already knew it could. The question was governance: two engineers had independently vibecoded internal tools that colleagues had started depending on, and nobody could say who owned them, whether they were secure, or what would happen when they broke. Leadership wanted a policy, and they wanted it grounded in evidence rather than opinion.
So we tested it properly, on four real candidate concepts the team had been arguing about for a quarter.
Decision path
Six days of vibecoding produced four working prototypes: an editorial archive search, an internal ad-inventory dashboard, a rights-clearance checklist tool, and a transcript summarisation utility. All four were clickable, populated with realistic sample data, and put in front of eleven internal users on day five. Traditionally each of these would have been a two-to-three-week spike, and the team would have built at most one before committing.
What the prototypes settled: the archive search was killed — users could already do it faster with existing filters, and the prototype made that obvious within four sessions; the rights-clearance tool was killed — every user asked for the same missing capability, which required a data source the group does not license; the ad-inventory dashboard was parked — genuinely useful, but overlapping with a vendor module already under contract; and the transcript summariser was validated and taken forward.
Envion contribution
On day six we ran a deliberate security and quality review of all four prototypes — to document the gap, not to criticise the tool. The findings were consistent and unsurprising: API credentials embedded in client-side code in three of four prototypes; no authentication or authorisation model in any of them; no test coverage and no error handling beyond the happy path; direct, unparameterised query construction in two; dependencies pulled in without version pinning or licence review; and no logging, no observability, no migration path for the data already entered.
None of these are arguments against vibecoding. They are arguments against promoting vibecoded work without a rebuild — which is precisely what had been happening informally.
Delivery
The rule that made it work: prototypes are disposable, and the disposal is scheduled at creation. Every prototype was created in a separate repository, marked with an expiry date, deployed only behind internal SSO on non-production infrastructure, and populated exclusively with synthetic data. All four were deleted on day seven.
The validated concept was then rebuilt from scratch in five weeks by the client's own engineers — informed by the prototype and the user findings, but sharing no code with it. The prototype's real output was the specification, not the software. The sprint ran with one AI strategy lead, two engineers, one designer and one security reviewer, and produced the four prototypes, the user-test findings, a one-page disposal policy and the production build of one concept.
Outcome and evidence
Four concepts validated in six days, two eliminated before build, one parked, one shipped — with roughly five months of speculative build effort avoided (modelled across the two killed concepts). Zero prototypes were promoted to production — by design — and the review documented six categories of critical issues across the four generated codebases.
Practical rules from the project: decide the exit before you start — every prototype gets an expiry date at creation; use synthetic data only; isolate it — separate repository, separate environment, internal access only; judge it on decisions, not artefacts — a prototype that gets a concept cancelled has done its job perfectly; never promote generated code — rebuild from the learning; watch for the quiet dependency — the real risk is a good prototype that colleagues start relying on while nobody is watching; and write the policy after your first sprint, not before — grounded in your own review findings, it will be one page and people will actually follow it.
Evidence gate. This page publishes only what Envion's project records and client disclosure permissions support. Outcomes are added once verified against a baseline, a measurement period, and an approved source.
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