AI-Assisted Engineering vs Vibe Coding: Specs, Reviews, Tests and Gates

The short answer

Vibe coding is a fast way to find out whether an idea works. The risk starts when the demo quietly becomes the product. At that point the code has to be changed, fixed and trusted by people who did not write it, and that needs things vibe coding skips: a specification, a person reviewing every change, tests, checks before release and a way to undo a bad one.

None of that means giving up AI tools. We use AI coding assistants in our own work. The difference is the process around them.

This article explains both terms fairly, where vibe-coded apps usually struggle, what engineering adds, and how to decide whether an app built this way is worth fixing or better rebuilt.

What vibe coding means

Vibe coding means describing what you want to an AI tool in plain language and letting it write the code, often without reading that code closely. You judge the result by whether it seems to work, then ask for changes until it does what you want. Collins named “vibe coding” its Word of the Year for 2025, and credits the AI researcher Andrej Karpathy with coining the term.

Tools such as Lovable, Bolt, Replit and Cursor have made this very accessible. A founder can describe an app on Monday and show a working version to customers by Friday. That is useful. It is a cheap way to test an idea, try a workflow, or build a small tool for yourself.

The tools are not the problem. The issue is what happens next.

What AI-assisted engineering adds

AI-assisted engineering uses the same kind of tools, but inside a process that assumes the code will have to be maintained. The AI writes and suggests. A person decides what gets built, reads what was written, and is responsible for it.

In practice the difference looks like this:

• Vibe coding: the goal is “does it seem to work?” The code is accepted if the screen looks right.
• AI-assisted engineering: the goal is “will it keep working when we change it?” The code is accepted when it matches the specification, a person has reviewed it and the checks pass.

Neither is wrong in its place. A prototype built to learn something is a good use of fast tools; that is the idea behind our AI-assisted prototyping, where a working prototype is built so that bugs and design flaws show up before engineering starts. The mistake is treating the prototype as finished software.

Where vibe-coded apps usually struggle

When an app built this way gets real users and real data, the problems tend to show up in the same places. These are areas to check, not a list of faults every such app has:

• Structure and duplication. The same logic appears in several places, so a fix in one spot leaves the bug alive in another.
• Error handling. Failures are swallowed or shown to users as raw messages, and nobody is told when something goes wrong.
• Data and access. It is unclear what is checked before data is saved, and who is allowed to see or change what.
• Tests. There are none, or they test that the code runs rather than that it does the right thing.
• Deployment and rollback. Changes go straight to the live app, and there is no simple way back.
• Dependencies. Packages were added as the AI suggested them, and nobody knows which are still needed or up to date.

None of these is unique to AI. Hand-written code that skipped the same steps has the same problems. Vibe coding just gets you to that point much faster.

Specs, reviews, tests and gates

Engineering adds a handful of habits. Each one is simple; together they are what keep code easy to change.

1. A specification before the code

Write down what the feature should do, what it must not do, and how you will know it works. With AI tools this matters more, not less: the tool will build exactly what you asked for, so the asking has to be precise. A short written spec also gives the reviewer something to check against.

2. A person reviews every change

Every change the AI produces is read by a developer before it is merged. The reviewer checks that it matches the spec, fits the existing structure, does not duplicate something that already exists, and handles failure properly. If nobody can explain a change, it does not go in.

3. Tests that describe behaviour

Automated tests record what the code is supposed to do, so the next change cannot quietly break it. They also make AI tools more useful: an assistant can be asked to make a failing test pass, and the test says objectively whether it did.

4. Gates before release

Tests, code style and static checks run automatically on every change, and the change cannot be merged until they pass. A check that was skipped must never be reported as a pass. We wrote about how we keep these gates fast in speeding up CI on GitHub Actions.

5. A way back

Releases go out in a way that can be undone. When something goes wrong, the first step is getting back to the last good version, not debugging on the live app.

These are the same principles behind building a website like a software system: structure first, so that change stays cheap.

Should you fix it or rebuild it?

If you already have an app built with AI tools and it is getting harder to live with, the first question is whether to repair it or start again. A rebuild is not automatically the grown-up answer, and repair is not always the cheap one.

It is usually worth fixing in place if:

• The app does what your users need, and the problems sit in specific areas.
• The data model mostly matches how your business works.
• Most of the fixes you need are small and local.
• Someone can explain how the main parts fit together, even roughly.

A rebuild is worth considering if:

• Every change breaks something unrelated.
• The data model does not match how your business works, so every new feature fights it.
• Fixing it would mean rewriting most of the code anyway.
• Nobody can say with confidence what parts of the code do.

Most apps sit somewhere in between. Often the right answer is to keep what works, fix the riskiest areas first, and replace one part at a time. A rebuild can also reuse what the first version taught you: the screens, the workflows and what users actually did with it.

How we work

We use AI coding assistants to write and review code faster. A developer checks every change, it is tested before release, and you get notes on what changed. Each piece of work is scoped and priced before it starts.

If you have an app built with AI tools that is now hard to change, we can review it and help fix it. The review ends in written findings ranked by severity, with what to fix first, and tells you plainly if a rebuild makes more sense. We do not sell vibe coding itself; we do the engineering that comes after it.

If your team wants to work this way, our workshops cover using AI assistants for research, drafting and code, with a person reviewing the result.