The Vibe Coding Model: How AI Complements Website Development

More companies than ever are leaning into vibe coding, and it’s easy to see why. AI can draft components, suggest logic, and troubleshoot errors in minutes. For teams trying to move faster, agentic coding fundamentally changes development speed.

The idea behind vibe coding is simple: you describe what you want, and an AI model produces the code. In some cases, the human role shifts from writing and reviewing code to prompting and deploying it. 

From a resource standpoint, the logic behind vibe coding makes sense. Hiring experienced developers takes time, and budgets are tight. AI can generate functional output quickly, helping teams explore and execute ideas sooner. Our team is actively embracing these tools, using clear guardrails and disciplined workflows to make that speed sustainable. 

The potential cost of this promising shortcut comes from how quickly changes can outpace thoughtful design. For instance, code that runs in isolation might struggle when integrated into a larger system. Security decisions may get overlooked. And performance tradeoffs pile up when no one steps back to assess the architecture as a whole. 

Development is moving faster with AI, and the question isn’t whether to use these tools, but how to use them well. In this article, we’ll explain what vibe coding looks like in practice and how our team is using the tech with proven success.

What is Vibe Coding?

Taking a step back, vibe coding is a development style where developers describe what they want to build, and an AI model generates the working code. This approach moves progress forward through rapid iteration.

In some interpretations, vibe coding includes minimal developer involvement. The person prompting the model may not review the code or even know how to code. As a result, they can’t accurately review or evaluate it. Although everything seems to work functionally, the underlying architecture isn’t fully understood.

That distinction between developer-guided work and code generated without technical review is where outcomes start to differ. Teams led by experienced developers tend to validate AI output, while business requirement-led teams often encounter stability and maintenance issues later.

Using AI as an assistant is different from handing it total control. AI can support good development when humans guide the process and refine each iteration. But when the model drives the build, risk compounds.

The second the code goes live, it becomes your team’s responsibility. If it exposes sensitive data or destabilizes the system, your organization will answer for it. While an AI model generated the original code, accountability remains with whoever chose to ship it.

The Risks of Vibe Coding

Most challenges tied to vibe coding don’t show up during the initial build. They tend to appear much later on, after users have begun to interact with the system. In our experience, teams tend to encounter issues in four common areas:

Security Vulnerabilities

AI-generated code can look clean and functional while still missing important safeguards. An exposed API key or loosely defined permission may leave sensitive data vulnerable and affect customer trust or compliance. These issues are much easier to prevent than they are to unwind after deployment.

Performance Dips

Just because lines of code “work” on a technical level doesn’t mean they won’t still slow your site or app down over time. Extra dependencies and inefficient queries may pass initial testing, yet still affect load times during upticks in traffic and usage. Without wise intention, the habit of outsourcing even seemingly small decisions to AI can add up to impact user experience and your resulting conversion rate.

Site Maintenance

Speedy output without architectural discipline often produces code that’s difficult to understand or work from in the future. Over time, debugging takes longer, and even minor changes feel risky.

Integration Gaps

Most site features rely on connections between systems. In practice, those connections might include your analytics platform, your CRM, or internal tools. When your integrations lack intentional design, data flows lose accuracy. In other words, integrations may function on their own, but their value drops if the data flowing between them isn’t reliable.

The Myth of Automated Code Development

You’ve likely seen recent predictions like Anthropic CEO Dario Amodei’s message to the World Economic Forum in Davos. In an interview with The Economist, Amodei suggested AI models could do “most, maybe all” of what software engineers currently do within six to 12 months. 

As any experienced developer knows, however, writing code is only one piece of web and application development. You also have to define the structure that will keep the platform stable as it evolves. These choices will dictate performance long after a product ships.

There’s no doubt that AI can speed up repetitive work given the proper structure and direction, but the tradeoff is steep. At the end of the day, AI is unlikely to ever fully grasp the nuances of your ecosystem or how implementing certain shortcuts might affect long-term stability.

The distinction becomes even more evident when you look at what development involves (and doesn’t):

• Writing code ≠ owning the architecture
• Passing a test ≠ designing a scalable system
• Generating output ≠ maintaining technical health

A feature that meets its requirements shows progress. But does it answer whether the system will be reliable over time? We expect a resounding No! from any developer reading this. That’s because development teams know they carry responsibility for those outcomes, and guiding the trajectory of that work remains a uniquely human responsibility.

Using AI in Development Without Sacrificing Stability

In our experience, AI works best as part of a disciplined workflow. Writing code faster can feel productive, but long-term site performance depends on careful review at every step. When AI supports development, you retain control over the direction of the work. 

One familiar example of this is test-driven development, a practice in which teams define expected functionality before writing any code. Instead of diving into an ocean of code all at once, developers start by writing a simple test that describes the behavior they want to see. Developers then write code until the test passes and refactor it to keep the system understandable as it grows.

Test-driven development works well alongside AI. By generating code quickly, LLMs can prove quite helpful during the “make it pass” phase. With the guidance of a human developer, they’re often especially useful for refactoring. The structure still comes from human decisions about how the system should evolve. In this use case, AI supports execution rather than replacing it.

We recommend implementing guardrails to protect performance, security, and maintainability, such as the following:

• Reviewing AI-generated changes before deployment
• Managing credentials and sensitive data carefully
• Testing updates in staging environments
• Tracking changes through version control
• Monitoring performance over time and after each release

These steps reduce avoidable issues and streamline future updates.

Vibe Coding Patterns to Watch

From our experience, teams building with vibe coding often encounter familiar signals. Early progress is smooth and development feels easier. Yet over time, struggles can surface as your ecosystem grows:

Development Updates Take Longer

Nothing dramatic happens overnight; routine work simply starts taking longer. For example, a small change affects an unrelated feature, or someone hesitates to edit code because the impact isn’t clear.

Code Context Becomes Hard to Follow

As AI-generated code stacks up, decisions begin failing to align. In order to address this, development focus shifts from building new capabilities to figuring out how existing pieces work together.

Confidence in Releases Drops

Eventually, hesitation creeps into everyday decisions. Your team begins to double-check releases more than before. Conversations change from “what should we build next?” to “what might this break?”

Building Intentionally with AI

Coding with AI introduces a new pace to development. Speed increases, but having a thoughtful process becomes even more pertinent.

At Chek Creative, we embrace AI as part of our development process. Our approach focuses on a handful of core practices:

1. We work in small, intentional steps instead of large sweeping changes. 
2. Code is always reviewed. 
3. Work begins with clearly defined issues that outline expected behavior and reduce ambiguity for both developers and AI tools. 
4. Planning and building remain separate, so decisions stay intentional as implementation moves forward. 
5. Test-driven development keeps progress steady and reliable. 
6. AI supports multiple parts of the workflow, including code review, while human judgment guides every decision.

With these practices in place, AI stays a helpful part of our workflow. Changes are introduced in small steps, and all decisions are rooted in experience.

If you’re working through how to use AI, we’re here to help with site and application development. Get in touch with us to start the conversation!