Website development with vibe coding

For a service website, organic traffic is also taken into account. Separate landing pages, meta tags, correct H1-H3 tags, internal links, and a clear URL structure are essential. If these requirements are addressed from the start, AI helps speed up technical implementation without interfering with subsequent SEO.

Once launched, the website can be developed in the usual way. Adding new sections, integrations, and forms doesn't require regenerating the entire project. The key requirement is a clear architecture and the absence of chaotic, duplicate components.

Corporate websites and promotional websites

Corporate websites typically contain more repetitive elements, so generating components is a significant time-saver. The header, service cards, forms, case study elements, and content blocks can all use a common design system. AI helps quickly translate this system into code and apply it across multiple pages.

However, website content requires special attention. The model doesn't know the company's actual benefits, real-world case studies, terms of cooperation, or service limitations. This information must come from the business itself; otherwise, even a carefully designed page will appear generic and inauthentic.

A promotional website is often required for a specific date or advertising campaign. Here, the ability to quickly put together a first version is especially valuable, leaving more time for scenario testing, analytics, and adjustments before the ad launch.

MVP for startups

A startup rarely knows the final version of its product before its first users. Therefore, the lengthy development of a large set of features creates additional risk. Vibe coding helps to quickly achieve a working MVP and test the core functionality with real people.

The first version can be built around one key task. After the launch, the team monitors where users stop, what actions they perform, and what they are missing. Subsequent iterations are based on this data, not just the founders' initial assumptions.

With this approach, technical discipline remains essential. Even an experimental MVP must be stored in Git, important decisions must be documented, and the database structure must be maintained. Otherwise, it will be difficult to turn a successful prototype into a viable product.

Small SaaS and web applications

A small web application might include authorization, a user account, a dashboard, a calculator, a document generator, or a simple booking system. Many such features are well described by formal requirements and are suitable for AI-assisted development. The model accelerates the typical components while a specialist verifies the overall logic.

Particular care is required when working with data. It's important to determine in advance who can see records, who can change them, and where sensitive information is stored. An error in the interface is usually immediately noticeable, whereas an error in access rights can go undetected for a long time.

As the service begins to grow, the architecture is revised as needed. Some of the AI-generated code can be refactored, individual libraries can be modified, and queries can be optimized. A healthy project structure makes such changes a routine development task.

Internal tools for business

An internal tool often doesn't require a complex public interface, but it should save employees time. It could be a mini-CRM, a request tracking system, an administrative panel, or an internal catalog. For such tasks, speed of initial implementation is often more important than a complex design.

First, describe the current workflow and determine which actions should be automated. After that, you can create a simple interface, connect the data, and test it with your employees. Their feedback helps you quickly remove unnecessary steps and add missing fields.

Further development depends on actual usage. If the tool becomes part of daily work, roles, reports, notifications, and integrations are gradually added. The initial version should not hinder further scaling.

What AI tools are used for vibe coding?

There are several classes of tools for vibe coding, and a single service rarely covers all tasks equally well. Some platforms build interfaces faster, while others work better directly with the repository. The choice depends on the stack, the size of the codebase, and how deeply the AI needs to work with the project.

When choosing a platform, consider the ability to export code, work via Git, connect to a backend, and leverage existing components. For a commercial project, data control and the absence of unnecessary dependencies on a specific platform are also important.

Full-stack AI builders

Lovable, Bolt, Replit, and v0 help you quickly get the first working version of an interface or a small app. They're suitable for prototypes, landing pages, and MVPs, where you need to see results early on. Some services can connect to a database and perform simple backend tasks.

The main advantage of the builder approach is the short path from idea to a working interface. However, before commercial launch, the code still needs to be reviewed. This especially applies to the project structure, security, and those components automatically hidden behind a user-friendly visual interface.

For a complex product, a builder may be used only at the initial stage. After the idea has been validated, the source code can be further developed using a regular development environment, if the chosen platform allows it.

AI editors and coding agents

Cursor, Claude Code, and Codex work more like regular development. They access an existing project, read multiple files, and make changes directly to the codebase. This format is convenient when developing an existing product.

The AI agent can find the required component, change the API, and update related types in a single iteration. The developer then reviews the diff and verifies the result. This workflow works well with Git because each group of changes remains visible.

For a large project, context management is critical. The more files an agent modifies simultaneously, the more difficult it is to quickly verify the result. Therefore, even powerful coding agents should be assigned limited tasks with clear completion criteria.

Why is a tool selected for a given task?

For a landing page, interface speed may be important, while for an existing SaaS system, understanding a large codebase may be crucial. A project with numerous integrations will require tools that work well with the backend and APIs. Therefore, the toolkit is determined after analyzing the task.

The project stack is also taken into account. If a site already runs on a specific framework, there's no point in generating a new parallel architecture for the sake of a single AI service. New functionality should continue the existing technical logic.

In a long-term project, the ability to work with regular source code is essential. The team must have access to the repository, change history, and infrastructure, regardless of which AI contributed to the initial version.

Vibe coding, no-code, or custom development: which to choose?

These approaches solve similar problems in different ways. No-code allows for product assembly within a ready-made platform, vibe coding speeds up work directly with code, and traditional development provides maximum manual control. The approach is chosen based on the project's requirements and post-launch plans.

For a simple internal service, no-code is sometimes sufficient. For an MVP with a custom interface and potential for further development, vibe coding may be more convenient. A complex system with numerous integrations requires strong architectural work, regardless of whether AI is used.

This table provides a general guideline but is not a substitute for evaluating a specific project. One small SaaS may thrive after a vibe coding MVP, while another will require a complex infrastructure and a dedicated backend team even in its first version.

When is it best to use vibe coding?

This approach is well suited when the first version needs to be quickly presented to users. This could be a landing page, a service website, an MVP, a prototype, or a small internal tool. It is especially useful for projects where features can be added in short iterations.

Vibe coding web development is also convenient when requirements change frequently. The team quickly tests a new version of the interface or logic and then decides whether to develop the idea further. This process reduces the cost of errors in the early stages.

AI can be used selectively within an existing product. For example, to speed up the creation of an administrative screen, a new report, or local automation, while maintaining the rest of the architecture.

When is it better to choose full-fledged custom development?

Classic development is preferable if the system initially has a complex architecture and a large number of dependencies. This applies to high-load services, critical operations, complex roles, and infrastructure with strict fault-tolerance requirements.

AI can still help the team write code, refactor, and perform routine tasks. Only the role of generation changes: it becomes part of the regular engineering process and does not determine the project's architecture.

The same applies to a product that needs to be developed over many years. The higher the cost of a technical error, the more attention is required to design, testing, and documentation, even before the first version.

What benefits does vibe coding bring to businesses?

The main practical advantage is related to the speed of iteration. The developer receives a working version of the feature faster and moves on to testing sooner. This allows the business to discuss a working interface rather than spending weeks agreeing on an abstract description of the future product.

Saving time doesn't automatically mean cutting development time several-fold. Some tasks are significantly sped up, while architecture, integration, and testing still require time. Therefore, it's worth evaluating the entire cycle up to stable production.

Faster launch

The first working version is delivered faster when most of the standard code doesn't have to be written manually. This allows for additional time for user testing, adjustments, and content preparation. For a project with a fixed launch date, this difference can be significant.

A quick launch is especially useful for a new service or hypothesis. Instead of developing a large product, the team releases a minimal scenario and observes real user behavior. This makes it easier to decide on the next budget.

At the same time, speed shouldn't compromise mandatory checks. Code review, QA, security, and technical SEO checks still have to be done before publication.

Rapid iterations

A project rarely remains unchanged after the first version. Users discover unclear areas, the business changes the terms, and the team discovers new ways to improve the scenario. AI helps make local edits faster and compare different options.

A developer can change a component, add a field, or rework logic in a few short iterations. The key is to commit successful states to Git and avoid mixing too many changes in a single prompt.

This workflow is also convenient after launch. Small improvements can be implemented regularly, without turning each update into a separate, lengthy project.

Less manual routine work

Development involves many repetitive operations: creating standard markup, interfaces, handlers, and utility functions. AI takes over some of this work, reducing the time spent on mechanical actions. The developer, meanwhile, focuses on verification and decisions that require context.

The savings are especially noticeable in projects with repetitive components. If you need to create several similar pages or administrative forms, the model can use an existing template and quickly adapt it to new data.

However, automatic generation requires consistency control. Without it, a project ends up with several similar components that perform the same function in different ways.

Ability to quickly test an MVP

An MVP requires speed to test a specific hypothesis. It's important for the business to understand whether users need the product and whether they're willing to perform the target action. Full functionality at this stage can only delay the response.

With vibe coding MVP development, the first version can be limited to a key scenario and gradually expanded. After launch, it becomes clear which features truly require development and which can be removed from the initial plan.

This process reduces the risk of spending large amounts of money before receiving feedback. Funds are allocated to features that already have a practical purpose.

The source code remains the basis of the product

If a project is built on a standard stack and stored in a repository, further development doesn't depend on the use of AI. The code can be read, modified, migrated to other infrastructure, and refined manually. This provides more freedom than some closed, no-code platforms.

Specific terms depend on the tools and the development contract. Therefore, before starting a project, it's important to determine where the repository is stored, who has access, and how the results will be transferred.

The sooner these rules are established, the easier it is to maintain the project after launch and involve other specialists in it.

What are the risks of AI development?

The main risks arise when generation speed is mistaken for the quality of the finished product. A model can generate a large amount of code in a few minutes, but the number of lines says nothing about the architecture, security, or maintainability. Bugs may only appear after several subsequent iterations.

Therefore, AI development requires a robust engineering process. Git, reviews, tests, and a clear architecture remain essential. The faster the code is generated, the more important it is to maintain control over changes.

Technical debt

AI sometimes solves similar problems in different ways. One helper might appear in one component, a nearly identical one might be created in another, and a third function might get its own library. When the project is small, this might go unnoticed.

After several dozen iterations, such decisions become more difficult to maintain. A change to one function must be repeated in several places, and it takes a new developer longer to understand the structure. Regular refactoring helps prevent this problem from escalating.

Technical debt is easier to remove gradually. If you postpone cleanup until the end of a large project, the amount of rework can become comparable to a single development stage.

Bugs and vulnerabilities

A model can write syntactically correct code with faulty logic. For example, access checks are performed only in the interface, while the server endpoint remains open. Such errors aren't always visible during a normal click on the page.

Secrets are another risk. AI should not add API keys directly to public code or log sensitive information. Environment variables and access rights require separate verification.

Any function that handles payments, accounts, or personal data requires stricter review. Automatic generation here speeds up code writing, but does not replace security testing.

Losing the context of a large project

The larger the project, the more difficult it is for AI to consider all existing solutions. The model may miss an outdated helper, a separate data access layer, or a condition that is located far from the file being modified. The result is a locally functional, but architecturally redundant implementation.

This problem is mitigated by formulating tasks in small chunks and clearly identifying related files. Good documentation and a predictable project structure also help the AI perform more accurately.

The developer remains the source of overall context. They know the history of decisions and can spot a change that technically works but violates the accepted architecture.

Scaling issues

A prototype might work perfectly with dozens of test users, but then start to slow down under real-world load. This can be due to suboptimal database queries, excessive API requests, or an overly heavy frontend. AI can't always predict real-world usage conditions in advance.

Scalability is therefore tested separately from functionality. The team analyzes bottlenecks, caching, queries, and infrastructure. If the product grows, some of the earlier solutions can be replaced with more suitable ones.

The mere use of vibe coding doesn't determine the scalability limit. The quality of the final architecture and how the project is maintained after the initial version are crucial.

How does Seo-Gen reduce risks?

In the workflow, changes shouldn't accumulate uncontrollably in a single version. The project is broken down into iterations, and successful states are committed to Git. This provides a clear history and simplifies troubleshooting.

Before changes move further, the code is reviewed by a developer. Testing is added for significant features, and the final version is tested in a live environment. This process is especially necessary when working with existing client projects.

Development in small iterations

A small task is verified faster and rolled back more easily. The developer sees a specific diff and can assess the impact of the change on related components. If the result is unsuitable, the fix doesn't affect half the project.

The iterative approach is also convenient for the client. They see the intermediate result and can adjust the functionality before new dependencies are added.

Git and revision history

Git preserves the sequence of edits and shows which files changed in each iteration. This is especially useful in AI development, where multiple sections of code may change in a single request.

History allows you to revert to a working version and compare implementation options. When working with a team, you can also see who made a change and why it was made.

Code review

Review is necessary even when a function appears to work correctly. The developer reads the code, evaluates its relationship with the rest of the system, and looks for unnecessary dependencies. This type of oversight reduces the risk of a buildup of random architectural decisions.

After the review, some of the code may be reworked. This is a normal stage of the process, especially if the initial implementation was created to quickly test an idea.

Testing

Testing checks not only the positive scenario but also errors. Forms are tested with invalid data, APIs with unsuccessful responses, and authorization with different user roles.

Browsers and mobile devices are tested separately for the website. If a function depends on a third-party service, it's important to ensure that the interface responds properly to its temporary unavailability.

Production check

The final test is performed on the production domain after deployment. This is where issues with the environment, CORS, HTTPS, email sending, or actual API keys become apparent.

After launch, SEO signals and analytics are also checked. The site is considered ready only after the main user scenarios are running on the production infrastructure.

Vibe Coding Agency or Freelancer: Which to Choose?

A vibe coding agency is convenient when a project requires development, design, QA, and SEO simultaneously. A single specialist can complete a small project, but the number of required skills increases with the complexity of the system. The choice depends on the size of the task and the requirements for ongoing support.

A vibe coding agency can distribute the work among specialists and maintain project knowledge within the team. This reduces dependence on one person and simplifies product development after launch. For a long-term website, it's also beneficial when SEO and development work within the same architecture.

A vibe coding company typically takes responsibility for the entire process, from task definition to production. This may include prototyping, development, code review, testing, and further refinement. The specific scope of work is determined before the project begins.

A freelancer is suitable for a limited task, especially if the requirements are already defined and a large team is not required. For example, this could be a separate landing page or an internal tool. When choosing a contractor, consider their Git workflow, testing, and the quality of the completed projects, not just the speed of generating the first version.

What should the team control?

The team defines the project's architecture and ensures that new changes comply with it. They check code quality, dependencies, business logic, access rights, and user data handling. Performance, responsiveness, and correct interface behavior are also monitored separately.

Technical SEO is also checked for the website. The page must be indexed properly, have correct meta tags, canonical tags, hreflang tags for multilingual support, a sitemap, and clear URLs. If the project uses client-side rendering, it is necessary to ensure that the search engine crawler receives the main content.

It's best to commit all changes to Git. Commit history helps you understand who changed what, quickly compare versions, and roll back any bad edits. Before going into production, the project undergoes testing, and after publishing, it's re-tested in a browser on the production domain.