What is Vibe Coding? Vibe Coding is a style of programming with AI in which you describe an idea in natural language and let an AI model generate the source code to build software. Instead of typing out every line of code, the developer focuses on the goal, describes the requirements and continuously refines the result together with the AI in a loop. The term was coined by Andrej Karpathy in February 2025 and quickly became a notable trend in the software industry. In the article below, TOT explains the essence, the way it works, the tools, and the benefits as well as the risks of vibe coding so that businesses have a complete picture. Learn more about software and AI solutions at TOT.
- The definition and origin of the term vibe coding (Andrej Karpathy, February 2025).
- The five core steps of how it works: describe the idea, the AI generates code, run a test, prompt again, then test and review.
- How vibe coding differs from traditional programming, AI-assisted coding and no-code/low-code.
- The benefits, the risks, and the cases where you should or should not use it.
- Popular tools and how to apply vibe coding in a business in a controlled way.
What is Vibe Coding?
Vibe Coding is a style of software development in which a person describes what they want in natural language, while a large language model (LLM) handles generating the corresponding source code. The person doesn’t write each statement directly but focuses on the intent and the output, then evaluates and gives feedback for the AI to keep adjusting. This approach places the human role at the “describe and direct” layer, while most of the syntax-writing work is handed to the AI.
Vibe coding, in Vietnamese, can be understood as “programming by inspiration” or “programming by description,” emphasizing that the user pursues an idea and the product experience rather than fussing over every technical detail. The term was introduced by Andrej Karpathy, an influential AI researcher, in February 2025. The core idea he described is to let the AI lead the coding process, with the user mainly conversing, observing the result and continuing to request changes. Technology organizations such as IBM and Google Cloud later offered similar definitions when discussing the concept.
It’s worth clarifying that vibe coding is not the same as AI helping to write a few isolated snippets of code. Here, the AI handles most of the process of producing functionality from a description, while the human coordinates through prompts and evaluates the whole. It’s precisely this shift that makes vibe coding a distinct way of working compared with ordinary programming, rather than just an autocomplete feature. To understand that boundary more clearly, the next section describes how vibe coding works in practice.
How does Vibe Coding work?
Vibe Coding works as a loop: a person describes the requirement, the AI generates code, the product is run and tested, the person gives feedback and the AI keeps adjusting until it reaches the desired result. Understanding how to use Vibe Coding step by step makes it clear which part is the AI’s work and which part the human must control. This article focuses on the principles and the big picture; if you need detailed hands-on guidance, you can refer to the article on building AI apps with vibe coding.
Step 1 – Describe the idea in natural language
The first step is to express what you want to build in natural language, as clearly as possible. Instead of starting with variable or function declarations, you write a prompt describing the goal — for example: “Create a task management page with a task list, an add button, a mark-as-done option and data saving.” The more specific the prompt is about functionality, target users and constraints, the more the AI has to work with to produce a result that fits your requirements. This is the stage that shapes the intent for the entire loop that follows.
Step 2 – The AI analyzes and generates code
At this step, the AI model analyzes the prompt, infers a suitable structure and generates the corresponding source code. Depending on the scope of the request, the AI can create various components of an application:
- Frontend: the user interface, layout and display components.
- Backend: the server-side processing logic.
- Database schema: the table structure and data relationships.
- API: the communication points between client and server.
- Components: reusable functional blocks.
- Configuration: config files, environment variables and project run settings.
This breadth of code generation is what sets vibe coding apart from merely suggesting a single line. Even so, the results still need to be verified in the later steps.
Step 3 – Run a test and give feedback
Once you have the code, you run it to check how the product behaves in practice. Your observation focuses on a few aspects: whether the UI displays correctly, whether the function runs as expected, whether the processing logic is sound, whether any errors arise and whether the performance is acceptable. The results of the test run become the basis for your next round of feedback, rather than reading every line of code carefully on the first pass.
Step 4 – Prompt again to make changes
When you spot something that falls short, you continue describing the adjustment you want and the AI updates the code. This is exactly the iterative feedback loop that characterizes vibe coding: describe, view the result, refine, then describe again. For instance, you might ask to “switch the layout to two columns,” “add input validation to the form” or “fix the bug when the list is empty.” Each loop brings the product closer to the original requirement without any manual rewriting.
Step 5 – Test and review
The final step is to test and review seriously; this is the stage that lifts vibe coding from a “quick experience” to a “usable product.” A person needs to reread the code the AI generated, check the business logic, review security, assess maintainability and run the necessary tests. For an experimental product or a personal tool, the level of review can be lighter; but for software going into operation, this step is mandatory to control quality and risk. It is precisely the review stage that ties vibe coding to engineering discipline rather than merely chasing speed.
How is Vibe Coding different from traditional programming and AI-assisted coding?
Vibe coding differs from the other approaches in the degree of the AI’s autonomy and the human’s role in writing, reading and controlling the code. The three tables below compare vibe coding with traditional programming, AI-assisted coding and no-code/low-code respectively, before pulling them together into a single combined table.
Vibe Coding vs. traditional programming
The biggest difference lies in who writes the code and the level of detail a person controls. Traditional programming puts the developer at the center, while vibe coding hands most of the writing to the AI.
| Criteria | Traditional programming | Vibe Coding |
|---|---|---|
| How code is created | The developer writes each line directly | The user describes the idea, the AI generates code |
| Skills required | Fluency in the language and framework | Knowing how to describe requirements and evaluate results |
| Prototyping speed | Slower, dependent on coding productivity | Fast, cycling through ideas continuously |
| Level of detailed control | High, with a firm grasp of every part | Dependent on prompt quality and the review stage |
Vibe Coding vs. AI-assisted coding
AI-assisted coding is when the developer still actively writes the code and the AI plays a supporting role, whereas vibe coding lets the AI lead most of the process. The boundary lies in whether a person writes, or merely describes and reviews.
| Criteria | AI-assisted coding | Vibe Coding |
|---|---|---|
| The human role | Actively writes, with the AI suggesting and assisting | Describes intent, the AI generates most of the code |
| Level of code comprehension | Reads and controls each section | Prioritizes the result, may not read every line closely at first |
| The AI’s level of autonomy | Assistance (autocomplete, suggestions, explanations) | Generates whole functional blocks from a description |
| Best fit | Boosting developer productivity | Quickly building ideas, prototypes, MVPs |
Vibe Coding vs. No-code/Low-code
No-code/low-code lets you build applications by drag-and-drop and preset configuration within a platform, whereas vibe coding produces real source code from a natural-language description. The difference is the output and the degree of customization.
| Criteria | No-code/Low-code | Vibe Coding |
|---|---|---|
| How you build | Drag-and-drop UI, configure preset blocks | Describe in natural language, the AI generates code |
| The output | An application running inside the platform | Real source code that can be customized more deeply |
| Customization limits | Constrained by the platform’s features | More flexible but requires technical review |
| Coding requirements | Almost no need to touch code | Still need to understand and control the code when going into operation |
The combined table below places the four approaches side by side to show where vibe coding sits in the overall picture.
| Criteria | Traditional programming | AI-assisted coding | Vibe Coding | No-code/Low-code |
|---|---|---|---|---|
| How the product is created | Code written by hand | Code written with AI assistance | Describe, the AI generates code | Drag-and-drop, configuration |
| Primary driver | The developer | The developer | The person describing the requirement | The business user |
| Source-code output | Yes, written by the person | Yes, written and reviewed by the person | Yes, generated by the AI | Limited or hidden |
| Prototyping speed | Slower | Faster than traditional | Very fast | Fast |
| Quality control | Actively comprehensive | Active, section by section | Concentrated in the review stage | Limited by the platform |
| Best suited for | Complex, long-term systems | Professional development teams | Prototypes, MVPs, experiments | Simple business applications |
The benefits of Vibe Coding in software development
The benefits of vibe coding don’t stop at “fast and easy”; they show up from both the developer’s and the business’s perspective: shortening the time to realize an idea, lowering technical barriers and broadening the ability to apply AI. Below are the most notable values.
Shortening the time from idea to prototype
Vibe coding helps turn an idea into a working test build in significantly less time than writing code by hand. Instead of scaffolding the project, configuring the environment and only then writing features, you can describe the requirement and get a prototype to interact with right away. This is very useful when you need to present an idea to your team or a client, or to check feasibility before committing large resources to the main development phase.
Lowering technical barriers when testing ideas
With vibe coding, someone who has an idea but isn’t yet fluent in programming can still build a usable version to test. Barriers around syntax, configuration and tooling are handled largely by the AI, letting product people, business analysts or founders focus on the problem they need to solve. As a result, the gap between “thinking it up” and “trying it out” narrows, encouraging more experiments at a lower cost.
Accelerating development and iteration
The describe–generate–feedback loop allows for fast iteration, so adjustments and improvements happen continuously. Each change can be tested almost instantly instead of waiting through many manual stages. For a development team, a high iteration speed means the ability to respond quickly to user feedback and market conditions, while reducing the wait time between versions.
Supporting MVP building with limited resources
Vibe coding is a fitting tool for building a minimum viable product when the team is still small or the budget is limited. The AI can generate most of the functional scaffolding, letting a small group still produce an MVP to validate a hypothesis with real users. To understand this concept, you can refer to the article on what an MVP is and how to identify the core features before you start building.
Expanding the application of AI in business
At the organizational level, vibe coding broadens how a business leverages AI in the software creation process, not stopping at chatbots or data analytics. Internal teams can quickly build automation tools, dashboards or utilities to support operations that previously had to wait for engineering resources. This helps spread a culture of experimentation and bring AI into more parts of the business.
A common mini use case: Idea → Prototype → User feedback → Adjustment → MVP. The business describes an idea, uses vibe coding to build a prototype, hands it to users to try and gather feedback, refines it based on the input, and progresses to an MVP good enough to validate the market before investing in full development.
Risks and limitations of Vibe Coding
Alongside the benefits, vibe coding comes with a number of risks that need to be taken seriously, especially when the product moves beyond the scope of experimentation. The five limitations below are points a business needs to control proactively.
AI-generated code can contain bugs
Source code produced by AI is not automatically correct and can contain hard-to-spot bugs. A product that appears to run on the first try can still fail on edge cases, unusual data handling or complex business flows. For example, a form might work with sample data but break when a user enters an unexpected value. If you skip the testing stage, these bugs easily slip into the live build and cause unpredictable consequences.
Security risks and data leaks
Quickly generated code can miss basic security principles, leading to vulnerabilities or the risk of data leaks. Missing input validation, exposing secret keys in configuration, or loose permissions are common problems when the focus is only on “making it run.” For a system that processes user information, a single security lapse can cause major damage, so a security review is mandatory before deployment.
Technical debt and maintainability
Rapidly churning out code prompt by prompt easily creates technical debt and makes the product hard to maintain later. Without a clear architecture, the code can be duplicated, inconsistent or patched together, making it costly to extend and fix. A product built quickly without being restructured may run for a while but becomes a burden as its scale and requirements grow.
AI hallucination and logic errors
AI can “hallucinate” — that is, invent libraries, functions or approaches that don’t exist or don’t fit — leading to logic errors. The model sometimes presents a solution very confidently but gets its essence wrong, especially with specialized business problems where it lacks context. If you trust it completely without verifying, these logic flaws can quietly make their way into the product and be hard to trace later.
Over-reliance on AI
Overusing vibe coding can make you over-reliant on AI and reduce your own ability to understand and control the system. When you need to handle a complex incident, optimize performance or change the architecture, not having a firm grasp of the AI-generated code becomes an obstacle. This dependence also brings risk when the tool or model changes, so human technical capability remains a key factor.
When you should and shouldn’t use Vibe Coding
Vibe coding is a fit for products that need to be built quickly and carry low risk, but it should not be the only approach for critical systems. Whether or not to use it depends on the context, the level of risk and the quality requirements.
Cases where you should use it
Vibe coding delivers value when the goal is speed and experimentation, for products that don’t yet demand absolute reliability. Suitable situations include:
- Prototypes and proofs of concept to validate an idea.
- MVPs with limited resources.
- Internal tools and automation scripts.
- A landing page or a simple dashboard.
- R&D activities, hackathons and personal tools.
In these cases, the cost of mistakes is low and the speed benefit far outweighs the risk.
Cases where you shouldn’t rely on Vibe Coding alone
For critical, sensitive or legally constrained systems, you should not depend entirely on vibe coding. Contexts that call for caution include:
- Banking/FinTech and financial systems.
- Healthcare and medical data.
- Enterprise ERP and core business systems.
- Systems holding sensitive or mission-critical data.
- Software that needs high scalability and strict compliance/security.
Here, vibe coding can be used for the experimental phase, but the final product must still go through a full engineering process.
A fitting model for businesses
The useful lens isn’t “vibe coding or not,” but “which layer of the software development process vibe coding sits in.” A business should position vibe coding as an accelerator at the front of the chain, then add the control layers of software engineering. A reference model: AI → Prototype → Developer Review → Architecture → Security → QA → Production. This approach keeps the speed at the ideation stage while still ensuring quality, security and operability when the product goes live.
How to apply Vibe Coding effectively in a business
Applying vibe coding effectively in a business means placing it within a controlled process, not replacing all engineering activity. Vibe coding should be seen as a capability within AI-assisted software development, accompanied by layers of review, testing and version management.
Define the scope of work the AI is allowed to handle
First, a business needs to clearly define which parts of the work the AI is allowed to take on. For example, the AI can build prototypes, create internal tools or generate functional scaffolding, but anything involving sensitive data, payments or core business logic needs tight human control. A clear boundary lets you leverage vibe coding’s speed while keeping the important control points.
Standardize prompts and context
Output quality depends heavily on the prompt and the context provided to the AI, so standardization is necessary. A business can build a library of prompt templates, conventions for describing requirements, and a set of context documents about the architecture and coding standards. When the context is standardized, the AI’s output is more consistent and requires less editing in the later steps.
Code and architecture review by a developer
All AI-generated code needs to be reviewed by a developer — for both the details and the overall architecture — before going into operation. The reviewer checks the correctness of the logic, its fit with the system architecture and its long-term maintainability. This is the key point for moving a product from a quick build to software that meets engineering standards.
Security and performance testing
Alongside the review, the product needs security and performance testing before deployment. Scanning for vulnerabilities, checking permissions, handling input and assessing load capacity help catch problems early that the rapid code-generation process easily misses. For a system serving many users, this is a step that cannot be skipped.
Bring the code into a CI/CD and version-management process
Finally, code from vibe coding should be brought into a CI/CD process and a version-management system like any other source code. Version control, automated build, test and deployment help track changes, roll back easily when something goes wrong and maintain quality over time. As a result, vibe coding blends into the existing engineering process rather than existing as a detached branch. For complex enterprise systems, TOT provides custom enterprise software development services to ensure the product meets standards for architecture, security and scalability.
Popular Vibe Coding tools today
There are now many tools that support vibe coding, from AI-integrated editors to platforms that build applications from a description. The table below summarizes some popular tools along with their highlights, best-fit users and main use cases; the descriptions are kept general to avoid overstating the features.
| Tool | Highlights | Best-fit users | Main use case |
|---|---|---|---|
| Cursor | AI-integrated editor with chat and an agent mode within the project | Developers | Writing and editing code with AI right in the editor |
| GitHub Copilot | A coding assistant that suggests and generates code, integrated in many IDEs | Developers and development teams | Speeding up coding and completing features |
| Claude Code | An agent-oriented coding tool that works in the terminal, using Claude models | Developers comfortable with the command line | Delegating coding tasks to the AI within the project’s context |
| Google AI Studio | Google’s platform for prototyping and experimenting with Gemini models | Experimenters and developers | Prototyping ideas based on Gemini models |
| Replit | A browser-based coding environment with a feature to build apps from a description | Beginners and small teams | Building and running apps directly on the cloud platform |
| Windsurf | An AI-integrated IDE with an agent mode that supports generating and editing code | Developers | Software development with context-aware AI support |
| Lovable | A platform for creating web applications from a natural-language description | Product people, non-coders | Quickly building web apps and prototypes |
| Bolt | A tool for creating full-stack web apps directly in the browser from a prompt | Founders, rapid product builders | Generating complete web apps from a description |
Each tool has its own strengths and audience, from supporting professional developers to creating apps for non-specialists. The right choice depends on your goal, the level of code control you want and your team’s existing process.
Will Vibe Coding replace developers?
Vibe coding won’t replace developers; it changes how they work. As the AI takes on most of the syntax-writing, the developer’s role shifts up to the higher layers of the software development process — where thinking and judgment that AI cannot yet fully replace are required.
Specifically, the focus of the work moves from writing code to a chain of capabilities: problem solving to understand and correctly frame the problem, architecture to design a durable system, AI orchestration to coordinate and exploit AI effectively, along with code review, testing, security and optimization to ensure quality. These parts become even more important as code-generation speed increases, because the product needs people in control so that bugs and risks don’t accumulate.
In other words, vibe coding raises productivity and expands the number of people who can take part in building software, but human technical capability remains the deciding factor for the final quality. Developers don’t disappear; instead they become the ones who direct, evaluate and optimize what the AI produces.
Frequently asked questions
What is Vibe Coding?
Vibe Coding is a style of programming with AI in which the user describes an idea in natural language and an AI model generates the source code to build software. The developer focuses on intent and outcome rather than writing each line of syntax, then continuously gives feedback for the AI to adjust in a loop. The term was coined by Andrej Karpathy in February 2025 and quickly became a notable trend in the software industry.
Will Vibe Coding replace developers?
No. Vibe Coding changes how developers work rather than eliminating them. As the AI takes on most of the code-writing, the developer’s role shifts toward problem solving, architecture design, AI orchestration, code review, testing, security and optimization. These capabilities become even more important as code-generation speed increases, because the product needs people to control quality and risk. Human technical capability therefore remains the deciding factor.
Is Vibe Coding programming without needing to know how to code?
Not exactly. Vibe Coding lowers the technical barrier because the user describes requirements in natural language instead of writing syntax, so someone not yet fluent in programming can still build an experimental product. However, to put a product into operation, understanding and controlling the code is still necessary to review for bugs, security and maintainability. So vibe coding is a fit for prototypes and MVPs, while critical systems still require genuine technical capability.
What are the popular Vibe Coding tools today?
Some popular tools include Cursor, GitHub Copilot, Claude Code, Google AI Studio, Replit, Windsurf, Lovable and Bolt. The group of AI-integrated editors and assistants such as Cursor, GitHub Copilot, Claude Code and Windsurf is aimed at developers. The group of platforms that build apps from a description, such as Replit, Lovable and Bolt, suits people who build products quickly. The choice of tool depends on your goal, the level of code control you want and your team’s process.
Should businesses use Vibe Coding?
Yes, but you need to place vibe coding within a controlled process. A business can use vibe coding to quickly build prototypes, MVPs, internal tools and to test ideas at a low cost. For sensitive systems such as finance, healthcare or core ERP, the product must still go through developer review, security and performance testing, and a CI/CD process. The sensible approach is to treat vibe coding as an acceleration layer, accompanied by the control layers of software engineering.
Conclusion
What is Vibe Coding? It is a style of programming with AI, where people describe an idea in natural language and an AI model generates the source code to build software, following a loop of describe – generate code – test – refine – review. Originating from the concept Andrej Karpathy raised in February 2025, vibe coding brings clear benefits in prototyping speed, lowering technical barriers and supporting MVP building with limited resources. However, this approach also comes with risks around latent bugs, security, technical debt and reliance on AI. For that reason, vibe coding should be positioned as an acceleration layer within the software development process, accompanied by review, testing and version management, rather than replacing software engineering. For businesses, defining the right scope and maintaining human control is the key to leveraging vibe coding sustainably and safely.
