Vibe coding: building an application with AI, without writing code

Vibe coding emerged in 2025 with code assistants capable of writing several files in a row and fixing their own errors. For a business leader, it changes one essential thing: the ability to test a software idea in a few days instead of a few months. It doesn't remove the basic rules of development. It shifts the effort from writing to describing, testing and reviewing. ## What vibe coding does well It excels on projects where the scope is clear and the risk low. A script that reformats an Excel file, a contact form with a database, an internal dashboard, a showcase site, a quote calculation page: these tools are built today in one or two days with a serious assistant. The gain is also educational. A business leader who describes their software learns to state their business rules precisely. This clarification is useful even if the code is ultimately taken over by a team: the specification is written while building. Some teams use it internally for repetitive tasks: generating test pages, migrating data, fixing typos in a repository, automating reports. The effect on productivity is real, provided the result is checked. ## Where you need a professional The limits appear as soon as you leave the prototype. The security of a form containing customer data, managing rights between an administrator and a user, the reliability of an app that receives orders all day: these topics require expertise that the assistant doesn't have on your behalf. Generated code can also accumulate invisible debt. A function has been duplicated, a slow query is repeated on every display, a secret key has been hard-coded in a file. The prototype works, then the app slows down from the first real traffic. A developer review, before public launch, costs far less than a recovery after an incident. The topic of dependencies matters too. Assistants often suggest trendy libraries, sometimes poorly maintained; every dependency must be checked and updated. Vibe coding gives the impression that the cost of an app is limited to a few prompts. In practice, the cost shifts rather than disappears. During prototyping, it drops sharply: an idea becomes clickable in a few hours instead of a few weeks. After launch, it rises again, in the form of fixes, security and technical debt. Two cases are very different. An internal application, a personal dashboard or a business tool used by three people don't need the same level of rigour as a platform that processes payments. The first case tolerates imperfect code; the second exposes the users, the data and often the company's cash flow. The time saved also has a counterpart: the time spent reviewing and testing. An experienced developer steering an AI assistant moves faster than a beginner, because they spot errors and know which question to ask. The quality of the result therefore depends directly on the ability to judge the code produced. ## A sensible method Rather than generating everything at once, proceed in small verifiable blocks: a page, a form, a database, then an integration. After each block, test on a real device, check error cases and keep a copy of the version that works. It is slower than a massive generation, but far less risky. Define from the start what is critical: authentication, payment, personal data, email sending. In these areas, professional support remains essential to check security and compliance. The rest — public pages, content, components — can be prototyped with AI, then consolidated. Finally, prepare the exit: a versioned repository on GitHub, deployment on Vercel, a Supabase database, automatic backup. These foundations cost little and avoid losing accumulated work. The right sequence for a business: prototype with an assistant, validate the need with real users, then decide. If the need is confirmed, entrust the building of a solid version to a team, handing over the prototype and feedback. If the need disappears, you lost a few days rather than a few months. Three habits prevent nasty surprises. Keep everything versioned with a private repository and backups. Ask the assistant to explain each important change in plain language, then check. And always test with realistic data, not perfect examples. For the next step, see the [Web applications & SaaS](/services/developpement-web/applications-web/) guide and the one on [AI agents](/services/data-ia/agents-ia/).

When this guide is for you

You want to test an app idea within days before investing
You have a simple internal tool to build (calculation, table, form)
You are a developer and want to speed up the repetitive part of the work
You want to understand the code a provider delivered before accepting it

Step by step

Write a precise project brief — Describe on one page what the app does, for whom, the screens, the data and the rules. An AI assistant produces much better code when the request is precise than when it changes with every message.
Start small and check at every step — Build one screen, test it, then move to the next. Keep the code under version control (GitHub) from the start so you can roll back when a change breaks what worked.
Test real cases, not just the happy path — Ask the assistant to list what can fail, then test: empty fields, wrong input, slow connection, user closing the page. This is where generated code proves incomplete.
Have security and hosting reviewed — A developer checks passwords, data access, forms and backups before public launch. A prototype can stay in your hands; a public site must be reviewed.
Decide when to move to conventional development — As soon as there are payments, regulated personal data, several simultaneous users or an obligation to be available at all times, a team takes over the code in a structured way.

What it costs (in MAD)

Service	Indicative price	Good to know
Prototype built with an AI assistant	1,500 – 8,000 MAD (≈ 140 – 730 €)	Subscriptions and time spent, with no development invoiced
AI development tool subscription	200 – 1,000 MAD per month (≈ 18 – 90 €)	Depending on the tool and usage volume
Developer takeover and securing	8,000 – 40,000 MAD (≈ 730 – 3,700 €)	Code audit, fixes, tests, go-live
Hosting and add-on services	100 – 800 MAD per month (≈ 9 – 73 €)	Server, database, email sending

Indicative ranges for the Moroccan market in 2026, excluding tax. Prices vary with scope and provider.

Mistakes to avoid

Putting a generated app online without reviewing data access or forms
Accepting every assistant suggestion without understanding the code produced: defects accumulate
Making increasingly broad requests with each message until you can no longer get back to a stable version
Forgetting error cases: AI codes the normal path better than the broken ones

Frequently asked questions

Do you need to know how to program for vibe coding?

No for a personal prototype. Yes for a public site, because you must at least understand the project structure, know what a database is and check access. A developer review is still advisable before any go-live.

Is AI-generated code good?

It is often correct for common functions and degrades on complex situations: fine-grained access rights, payments, real time, large volumes. Its quality depends above all on the precision of the request and how you test.

Do these tools replace a developer?

They change their work: less code written by hand, more review, testing and architecture. For a simple feature, an AI assistant saves hours; for a complete product, the share of human judgement remains essential.

Which tools should be used in Morocco?

Code assistants (Claude, Cursor) for those who can read code, and AI build platforms (website templates, forms, dashboards) for business profiles. Check billing in foreign currencies and data retention terms.

Version française: /insights/vibe-coding-construire-une-application-avec-l-ia/