What is vibecoding? In short: you describe the task in plain English, and AI writes the code for you. Then you run the result, see what’s broken, and ask for a fix. Iteration after iteration, and you have a working prototype .Vibecoding in programming is an approach in which part of the development process is delegated to a language model. Not to a code editor or your own hands, but to an AI agent that generates, edits files, and assembles logic based on your description. You don’t think through every line in advance—you formulate the desired result .
What is vibecoding in simple terms? Here’s a simple answer: imagine you have a very patient programmer who you can explain a task to in Russian, and they’ll write the code. You check it, say, “This is wrong,” and they’ll rework it. That’s pretty much how it works.
Why the term is understood differently
The term is new. Andriy Karpatyi publicly used “vibe coding” on February 2, 2025, and the word quickly spread through the media and developer communities. Since then, it has been interpreted in various ways.
In a narrow sense, it’s almost entirely prompt-based work, with minimal human interaction with the code. In a broader sense, it’s any development that relies heavily on an AI assistant . This leads to confusion: some believe that vibe coding means “handing over the wheel to AI,” while others think it’s simply a trendy name for familiar AI-assisted development. Frankly, both are right—it depends on the scenario.
Vibecoding in AI isn’t a specific technology of a single company, but a general pattern for working with language models to create programs. If you want to ask “Alice, what is vibecoding?” out loud, the usual answer will be the same: a way to create programs by describing an AI task.
A short definition in one paragraph
The idea is simple: you describe the task in natural language , the AI generates code, you run it and refine it. No “coding from scratch”—instead, you interact with the system that writes the code for you. Vibecoding, in simple terms, is precisely this approach, where AI handles most of the technical work.
Vibecoding
is a development where the user describes an idea in natural language , and AI generates and modifies the code.
AI-assisted coding
The developer works with the code themselves, and AI helps with hints, refactorings, and code snippets .
No-code:
Create apps without hand-coding using visual blocks and designers.
Low-code
development with minimal manual coding and visual tools, platforms like Bubble or Webflow.
AI agents
of the System that not only respond but also execute project-specific action chains—read files, run commands, and make edits.
How Vibecoding Works in Practice
In practice, it all comes down to the “described—received—launched—corrected” cycle. This is where the magic ends and the routine begins. Vibecoding works in iterations —and it’s important to understand this before you start, otherwise the first mistake seems catastrophic.
From the description of the idea to the first result
Step 1. Formulate the idea . Describe who the app is for, what problem it solves, and what result the user will receive. Example: “An app for a coffee shop owner. It tracks coffee and milk supplies. Automatically creates an order for the supplier when supplies run low.”
Step 2. Write a prompt structured as follows : what to create – for whom – required functions – technologies – readiness criteria.
Step 3. Generate the code and run it . Test the basic scenarios: adding, deleting, and editing. Enter any incorrect data. Open it on your phone and in different browsers.
Step 4. Refine using clarifying prompts . Describe the specific problem and desired outcome: “The ‘Save’ button in the form isn’t working. After clicking it, the data doesn’t appear in the table. Fix it so that the new record is added to the end of the list.”
Step 5. Save the version using export, snapshot, or git commit.
Two real-world examples. Cynthia Chen, a product designer at Block with no formal engineering background, created the Dog-e-dex mobile app in about two months using Claude AI. She described tasks in natural language: “add photo uploads,” “connect the image recognition API.” Breaking down the steps and precisely worded prompts solved the problem of instability in the early stages (she also had help from her engineer friends with the complex backend). The result is a working app for identifying dog breeds. Another example: a marketer spent three hours a day manually collecting statistics from five advertising accounts. Using vibecoding, she created a dashboard with automatic connection via API, cutting the reporting time to 15 minutes.
What happens after the code is generated?
After the first generation, the most important part begins. The production code needs to be run, tested, and any odd behavior identified. According to discussions in developer communities, after the first generation, users typically make several iterations of fixes . The most common issues that break include UI elements, state management, async logic, and component integration.
AI-generated code may contain security vulnerabilities if it is not reviewed by experts. CISA’s AI Cybersecurity Collaboration Playbook (2025) emphasizes the importance of collaboratively identifying and mitigating cyber risks associated with AI systems.
The practical conclusion is simple: AI-generated code should be considered a draft , not a finished product. Before using AI-generated code in production systems, it is recommended to conduct a professional security audit.
The Vibecoding Cycle: Idea, Code Generation, Launch, Editing
How does vibecoding differ from AI-assisted, no-code, and low-code coding?
There is a difference, and it’s important. Wibecoding is handing over the wheel to AI : you describe the result, and the model generates the core code. AI-assisted development is where you develop with AI: you design and write the basics yourself, and the AI provides suggestions and refinements. In no-code, you drag and click blocks, while in vibecoding, you simply describe what’s needed. Low-code refers to platforms like Bubble/Webflow with minimal coding, while vibecoding is AI-assisted code generation via Copilot/Cursor.
If we break down the four approaches by the role of humans, the role of AI, code control, and launch speed , the picture emerges as follows:
- Vibecoding. A human describes the result, and the AI generates and modifies the underlying code. Control over the code is lower, especially if the human doesn’t understand the codebase. The start-up speed is very high .
- AI-assisted. A human designer designs and writes the core code, while AI provides suggestions, enhancements, and refactoring. Code control is high , and startup time is fast.
- No-code. A human assembles an app from ready-made blocks; the role of AI is usually secondary. Control is high—but only within the platform . The launch speed is very high.
- Low-code. A human combines visual blocks and code, while AI helps speed up the build. Control is medium to high , and the start-up speed is high.
The key difference in vibecoding is the degree of delegation . The less you understand what’s going on inside the code, the higher the risk of losing control as the task becomes more complex.
Who is vibe coding suitable for, and who should avoid it?
The method is useful where the cost of error is low and speed is more important than perfection. Three groups are effectively distinguished: non-developers for prototypes, startups for a quick MVP, and experienced developers for speeding up routine tasks.
Where Vibecoding is Really Useful
Landing pages, calculators, chatbots. Internal tools and dashboards. MVPs for hypothesis testing —quickly build a prototype without extensive coding. Automation of routine tasks: parsing, reports, simple web applications. Educational projects and portfolios. Demo-level mobile apps are also feasible if the task doesn’t require complex business logic.
Where do restrictions begin?
Production systems with payments . Handling personal data without auditing. High-load services for thousands of users. Complex business logic with numerous edge cases. Products with security requirements – medicine, finance.
Important: AI-generated code should be considered a prototype , not a finished product. IEEE Spectrum warned of security bugs and hallucinated APIs in 2024, and InfoWorld warned of increasing maintainability issues in 2025. Before going into production, a professional security audit is required. This is not optional.
AI systems can introduce errors, biases, and security vulnerabilities that impact the quality and reliability of their code, so they require human oversight . This is stated by NIST in its AI Risk Management Framework 1.0 (2023).
The benefits of vibecoding: why people use it
Vibecoding has opened up unexpected opportunities for many
It’s not just a buzzword. Vibecoding saves time at the start and lowers the entry barrier—that’s a fact. When you need to quickly build a prototype, test a hypothesis, or automate a piece of routine work, AI truly speeds up the process.
If you compare the speed on typical tasks, the difference is noticeable :
- A typical development task—in a controlled experiment, it was completed approximately 55% faster with an AI assistant than without it.
- Creating a landing page – without AI it usually takes 2-3 days, with AI – 2-3 hours .
- Simple Telegram bot – without AI 1-2 days, with AI 1-2 hours .
“Participants in the GitHub Copilot experiment completed the task on average 55% faster than those without the AI assistant (1 hour 11 minutes versus 2 hours 41 minutes).” — GitHub / Microsoft, “Research: quantifying GitHub Copilot’s impact on developer productivity and happiness,” 2022. https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/
Another advantage is the lower barrier to entry for non-technical people . A marketer, analyst, or entrepreneur can build the first prototype themselves. However, it’s important not to confuse low entry level with zero responsibility for the outcome.
Cons and Risks: Why It Works Until It Breaks
The main drawback is the loss of control . When a person accepts AI-generated code without understanding it, they end up with lines they didn’t write and don’t understand. And then, any mistake turns into a quest without a map.
What breaks when the task becomes more complex
The more complex the task, the higher the chance that AI will misunderstand requirements, invent a non-existent API , or accumulate logical errors across multiple levels. One mechanism is explained in the study “Lost in the Middle” (TACL, 2024; preprint arXiv 2023): the model makes less use of information in the middle of a long context than at its beginning and end.
In practice, this looks boring but painful. First, there’s working code, then another feature is added—and suddenly the system falls apart . The code only seems coherent locally; globally, it’s chaos.
Why support and debugging are becoming a problem
Support suffers because humans are forced to navigate a codebase they didn’t design. It’s easier to generate than to maintain . This is especially true in environments where there are no tests, proper commit messages, or a clear architecture. Code written by AI without a version control system or documentation becomes a dark box at the first serious error.
Is this a scam or a working approach? Debunking the main myths.
No, it’s not a scam . But promises like “build an app and earn millions in a week” are exaggerated. It’s a marketing term that’s been hijacked by a real and useful approach.
“AI will completely replace developers” – no. NIST requires human oversight , and in large IT teams, review and merge remain the responsibility of engineers.
“A beginner just needs to press one button” – no. Even a simple scenario requires problem definition, testing, and iteration . Without this, it’s a mess.
“Wibecoding is suitable for any production code” – no. The OWASP Top 10 for LLM Applications and CISA warn of prompt injection, insecure output handling, and data leakage. These are real risks, not theoretical ones .
Vibecoding and the Job Market: What’s Changing for Developers
According to Russian surveys conducted in 2025, a significant proportion of specialists are already using AI tools in their work—several times more than the previous year. Most note that the automation of routine tasks has freed up more time for new functions.
The developer’s role is transforming . A single senior developer with an AI assistant increasingly handles tasks that previously required a team of several junior developers. New specializations are emerging: prompt code engineers, AI agent solution architects, and AI security engineers.
There’s also a warning sign for junior specialists : the traditional path through routine coding is being closed, and job openings for “experience” are becoming fewer. A strong career path now lies in understanding the fundamentals and effectively using AI tools in development.
Where to start for a newbie: the route to your first MVP
It’s best to start with a small, verifiable task . Not “I want a new marketplace,” but “I want a page, a form, a table, and simple data saving.” This is more straightforward and faster.
Which tools and AI models to choose at the start
There are many tools, and they vary in terms of accessibility in Russia , the availability of a free plan, and their target audience. Here’s a quick overview of the main ones:
- YandexGPT works in Russia without a VPN and offers a free plan. It’s good for beginners and Russian-speaking users.
- GigaChat (Sber) – works without a VPN , there’s a free plan. Also convenient for beginners and for Russian chats.
- Replit — the interface operates from Russia , the free plan is limited (about 3 projects). Suitable for browser-based development.
- Cursor — the interface works, and there’s a free plan. It’s convenient for local projects .
- GitHub Copilot is working, but free access is limited. It’s more for experienced developers .
- Lovable — requires a VPN , free plan is limited. Designed for fast web apps.
- Bolt.new – requires a VPN, offers a free plan. It’s ready for MVPs in minutes .
- v0 (Vercel) — requires a VPN, free plan available. Good for generating React components .
Payment for foreign services with Russian cards is generally unavailable . YandexGPT and GigaChat write code in Python, JavaScript, and Java, understand technical context in Russian, and operate without restrictions—a good entry point.
How to set a task so that AI doesn’t give you a “mess”
The main rule is not to ask for everything at once . Describe the task specifically: what the app does, who the user is, what screens are needed, where the data is stored.
Landing page example:
❌ Bad : “Make me a website for my business. Beautiful, modern, so clients come to me.”
✅ Good : “Create a landing page for a nail studio in Moscow. Blocks: header with logo and phone number, hero section with photo and “Book Now” button, 4 service cards with prices, gallery of work (6 photos), reviews (3), booking form (name, phone number, date), footer with address. Style: minimalist, pink and white tones.”
Example for a Telegram bot:
❌ Bad : “Write a bot for Telegram. One that’s smart and answers questions.”
✅ Good : “Write a Telegram bot in Python (aiogram 3). Functions: 1) /start — greeting and menu with buttons; 2) “Our services” — a list of 5 services with prices; 3) “Sign up” — the bot asks for name, phone number, and date, and saves them in Google Sheets; 4) “Contacts” — address and map link. Add error handling and logging.”
Clarifying prompts for iterations work on the same logic: a specific problem plus a desired outcome . “Add the ability to edit records. Clicking on a row opens a form with the completed data.” Without specifics, the AI guesses—and guesses poorly.
Comparing bad and good prompts for vibecoding
What tools are most commonly used for vibecoding?
Cursor is a code editor with deep AI integration , suitable for local projects of any complexity. Replit is a browser-based development environment, allowing you to get started without installing anything. GitHub Copilot is an AI assistant inside Visual Studio Code, suitable for experienced developers. Lovable and Bolt.new are fast AI-powered web builders, optimized for MVPs in minutes, but require a Russian VPN. Windsurf and Cline are alternative editors with agent mode, gaining popularity in 2025. For Russian-speaking users without a VPN, YandexGPT and GigaChat are the foundation, and Cursor is the development environment.
Frequently asked questions
Do you need a developer if the code is already generated by AI?
Yes, it’s often necessary. Especially if the project is going into production and deals with security, payments, and user data. The point of vibecoding is to speed up the launch and validate the idea , not to cancel the developer’s involvement in the next cycle. Support and scaling are a different story.
Who owns the AI code and can it be used commercially?
According to Article 1228 of the Civil Code of the Russian Federation , an author is defined as an individual whose creative work created the result. If AI generates code without significant human intervention, such code is not protected by copyright. Use in commercial products is permitted, but it is important to check the terms of the specific service, ensure that the AI has not reproduced a fragment with an incompatible license (GPL, AGPL), and comply with the requirements of Federal Law No. 152 when handling personal data.
Does Vibecoding Help You Learn Programming?
It can help if you analyze the output. But it hinders if you only copy the finished code. NIST specifically identifies the risk of overreliance— excessive trust in AI . Three tips for beginners: after each generation, ask the AI to explain how the code works, line by line; write a short fragment once a week without AI prompts to maintain the skill; read other people’s projects—not just your own generated ones. For those who want to systematically understand vibecoding and practice it on real projects under mentorship, it’s easier to follow a structured program than to gather knowledge piecemeal.
Does Vibecoding have career value in the market?
As a separate profession, no; there’s no formalized demand for “vibe coders” in job postings for 2025–2026. However, the value of AI skills in development is growing. Companies are looking for developers who use AI tools competently. This is stated in resumes as follows: “Experience developing with AI assistants (Cursor, GitHub Copilot): prototyping, refactoring, test generation.” This is more valuable than “I can communicate with a bot.”
How much does it cost to prototype via Wiebcoding compared to hiring a developer?
A landing page created through Vibecoding requires 2-3 hours of work plus a subscription from $0 to $20 per month. The same landing page created by a freelancer costs 15,000 to 50,000 rubles and takes 3-7 days. A web app MVP created through Vibecoding takes 2-5 days with a basic subscription. The same task with a developer costs from 100,000 rubles and takes 2-4 weeks. Vibecoding is 5-10 times cheaper at the start, but more expensive during the support phase if the code goes into production without an audit.
