"Vibe coding" has a nice ring to it. Throw a prompt at an LLM, accept whatever comes back, ship it. No architecture. No tests. No plan. Just vibes.
It works surprisingly well for a moment. Then someone asks "can we add authentication?" or "why does it return the wrong answer for this input?" and the whole thing unravels. You don't have a codebase. You have a pile of generated text that happened to run.
There's a different way to use the same AI tools at the same speed, but without building on sand. Call it vibe engineering: you still move fast, you still lean on AI heavily, but you keep the structural discipline that makes the result worth showing to a stakeholder, a user, or an investor.
What vibe coding actually produces
Let's be honest about what you get when you prompt-and-pray your way to a prototype.
No separation of concerns. The LLM gives you one file that does everything. Business logic, UI, data access, configuration -- all tangled together. It works for the demo. It's unmaintainable as soon as real work begins.
No error handling. The generated code follows the happy path. Edge cases, timeouts, malformed input, rate limits -- none of that exists until it blows up in front of someone who matters.
No tests. Not even a single assertion. You can't tell what the code is supposed to do, only what it happens to do right now.
No observability. When it breaks (and it will), you have no logs, no traces, no way to figure out what happened. You're back to prompting and hoping.
Implicit assumptions everywhere. The model made dozens of decisions you never reviewed: which library, which API pattern, which data format, which auth flow. You shipped all of those decisions without knowing they were made.
This is fine for a quick hack. It is not fine when the goal is to validate whether an idea is worth building for real.
What vibe engineering looks like
Vibe engineering keeps the speed. It drops the chaos.
The difference isn't about writing more code or using AI less. It's about applying a small amount of engineering discipline at the moments that matter most -- before you generate, while you generate, and right after.
Start with a contract, not a prompt. Before you ask the AI to build anything, define what the system should do and what it should not do. Inputs, outputs, boundaries. One page is enough. This tiny constraint saves a lot of rework because the AI now generates within a frame instead of inventing one.
Scaffold first, fill second. Set up the project structure, the module boundaries, the API surface. Let AI fill in the implementations. This is the opposite of vibe coding, where the structure is whatever the model felt like producing. When you control the skeleton, the generated code fits inside something you can reason about.
Test the critical path immediately. You don't need 100% coverage. You need a few assertions on the thing that matters: the core workflow, the main API endpoint, the key business rule. Write those tests before or alongside the generated code. Now you have a safety net that lets you regenerate and refactor with confidence.
Keep humans in the loop on design decisions. AI is fast at implementation. It's unreliable at architecture. Every time the model suggests a pattern, a library, or a data model, a human should glance at it and ask: "Is this the decision we'd make if we were building this to last?" You don't have to build it to last right now. But you should know what you'd change if you did.
Wire in observability from the start. Structured logs. Error tracking. Basic metrics. This takes a relatively small amount of effort and changes your PoC from "it works on my machine" to "we can see what it does in the real world." When your stakeholder asks "how is it performing?", you have an answer.
Why this matters for PoC development
A proof of concept has one job: answer the question "should we invest more in this idea?"
If the PoC is vibe coded, the answer is always muddy. It works, sort of, but nobody knows how fragile it is. Nobody can extend it. Nobody can point to the metrics and say "users did X, which tells us Y." The demo looks good in a meeting. It falls apart under any scrutiny.
If the PoC is vibe engineered, you get a different outcome:
- It's testable. You can put it in front of real users and trust the results because the system behaves predictably.
- It's legible. A new engineer can open the code, understand the structure, and make changes. The PoC is not a dead end; it's a starting point.
- It's measurable. You have logs and metrics that tell you what happened, not just whether the demo went well.
- It's honest. The PoC shows what works and what doesn't, instead of papering over gaps with generated code nobody reviewed.
The speed difference between vibe coding and vibe engineering is small. The quality difference is enormous. One gives you a demo. The other gives you a decision.
The discipline tax is lower than you think
Engineers often resist process during prototyping because they associate it with slowness. Stand-ups, planning poker, three rounds of code review -- that's not what we're talking about.
Vibe engineering for a PoC means:
- a one-page scope document,
- a scaffolded project structure,
- a handful of tests on the critical path,
- basic logging and error handling,
- a human review of key design decisions (ongoing, lightweight).
That's a modest amount of extra work on a prototype build. In return, you get a PoC that actually answers the question it was supposed to answer, and a codebase that can become the foundation for a real product if the answer is yes.
Compare that with the vibe-coded alternative: you ship quickly, spend the rest of the project explaining why it broke, and then rebuild from scratch anyway.
When vibe coding is fine
To be fair, there are times when pure vibe coding is the right call:
- Throwaway scripts. Data migration, one-off analysis, a quick format conversion. If nobody will ever run it again, don't engineer it.
- Personal tools. Your own productivity hack, your side project, your internal dashboard that only you use. Go wild.
- Exploration. You're not building a PoC yet; you're just seeing what's possible. Prompt freely, learn fast, throw it away.
The trouble starts when vibe-coded artifacts get promoted. When the throwaway script becomes the data pipeline. When the quick hack gets shown to a client. When the exploration becomes the PoC that drives a funding decision.
That's the boundary. If anyone besides you will depend on the output, apply the engineering.
Moving fast without moving reckless
The AI tools available today make it genuinely possible to go from idea to working prototype quickly. That's a real advantage -- for founders testing a hypothesis, for product teams validating a feature, for innovation groups proving out a concept.
But the tools don't care whether you use them well or badly. They'll generate a clean, well-structured system just as readily as a tangled mess. The difference is entirely in how you wield them.
Vibe engineering is not slower than vibe coding. It's the same speed, pointed in a direction that actually leads somewhere.
At Unllmited, this is exactly how we build proofs of concept: fast, AI-assisted, and engineered to give you a real answer instead of a fragile demo. If you have an idea that needs validating, take a look at our Rapid PoC Development service. And if you want your team building this way too, check out our Vibe Engineering Workshops -- or reach out directly and we'll scope it together.