Should You Vibe Code a Rich Text Editor?

With the widespread adoption of AI, businesses have seen some development projects speed up. That was the promise of AI-assisted coding.

Vibe coding does make initial builds cheaper and faster. What took weeks could take days (or hours). But in some cases, that speed stops being an asset and becomes a liability.

The real question isn’t whether AI can generate code, it’s whether your business should take ownership of that code long term. This turns it from a technical choice to a governance decision: Are you prepared to own the result even if development seems fast up front? Or would buying an editor from a proven vendor be a smarter option? This article will focus on the risks of vibe coding and why rich text editors, in particular, make these issues even more visible.

#What people mean by vibe coding

Vibe coding is another term for AI-assisted or AI-driven coding. In day-to-day practice, it’s called “vibe coding” because it resembles “coding by feel.” The process normally involves entering an AI prompt describing system functionality, pasting the output into your codebase, checking if it visibly works, and then iterating.

This prioritizes speed and visible results over traditional, formal software design. However, it comes at the cost of deep code knowledge, planning, and ownership, making maintenance a compounding technical debt. In other words, an application’s success is tied to how it displays initially, not how it performs over time.

Even with AI governance standards in place, teams are often stymied in their attempts at long-term maintenance. AI optimizes for making something work, but it doesn’t always choose the best structure (nor does it consider dependencies in other parts of the codebase). Teams must own code they didn’t architect, didn’t write, and don’t understand. That makes it harder to modify something in one place without breaking functionality elsewhere. Governance and reviews may catch obvious errors, but smaller problems can slip through and add up, making applications expensive to maintain over time.

#Why vibe coding works for some software

Vibe coding appeals to development teams under pressure to deliver quickly. This makes AI-assisted coding perfect for:

• Rapid prototyping: Your team can iterate on early feedback.

• Proofs of concept: If you’re a product manager and want to determine a feature’s feasibility or usability, then AI helps you test the waters fast.

• Internal, simple applications: With simpler applications that solve a focused need for the business, vibe coding gets something up and running quickly. This can be a game-changer for internal applications for underserved teams, leading to greater operational efficiency.

In other words, vibe coding works best when the cost of being wrong is low and fixing issues is cheap.

#Why rich text editors are a different class of system

So, can you vibe code a rich text editor? For some extensions and features, sure. But building a rich text editor from the ground up? That’s challenging enough even for experienced programmers due to rich text editors’ inherent complexity.

Here’s why. First, they’re central to business workflows. Rich text editors are used for everything from content management systems to customer support services to internal communications platforms. Mission-critical use cases like these have outsized impacts and carry corresponding risks.

Second, vibe coding a rich text editor can create compounding costs because they are used heavily and are critical to communications and collaboration. A small bug can lead to user frustration over time and slow productivity.

Plus, rich text editors are often web components that live on for years. This makes them expensive to maintain. Unlike software that focuses on one operating system, they must adapt to changes across browsers like Chrome, Firefox, Safari, and Edge, and across devices and screen sizes. Each browser update brings new changes and potential issues, which require fixes in a constantly shifting environment.

Third, failures in rich text editors can lead to ecosystem fragility because they’re usually connected to a wide swath of components and other applications, especially in enterprise environments.

Plus, AI coding usually optimizes for happy paths without solving for edge cases. Rich text editors are pretty much nothing but edge cases, from browser inconsistencies to cursor location issues to media embeds.

Rich text editors are complex, integral systems. In fact, they’re not really applications: they’re core infrastructure. That makes getting them right critical to reducing potential problems.

Simple rule of thumb

When you step back, a pattern emerges. AI-assisted coding can help your team move faster when done in the right contexts. But it’s not the tool for every job. Here’s a way to think of it:

• Use AI-assisted coding for demos, prototypes, or proofs of concept.

• Use AI-assisted coding, hand-coding, or a hybrid approach if it’s a simple, disposable, or low-risk tool that’s mostly used internally.

• If an application is user-facing, persistent, or central to business workflows, treat it as infrastructure. That calls for a team of internal developer experts or buying from a trusted vendor.

Rich text editors fall into the last category. They’re long-lived, complex systems that grow even more complex over time. At that point, the decision isn’t about speed; it’s about ownership.

#The hidden costs of vibe coding

The tradeoffs of AI-assisted coding become clear once the software hits real-world conditions. In many cases, the applications start showing their age fast and end up costing you extra time to fix or maintain.

#Workable code doesn’t mean shippable code

AI gets software working quickly. Someone gives directions via a prompt, receives outputs, and then tests them in a browser. It may work in a demo environment, but it can hide problems that surface later (which makes them harder and costlier to fix).

This isn’t theoretical. In a recent article, an AI-assisted development tool created a functional web browser, but required bug fixes and changes to work properly in practice. Several developers quoted in the article noted problems around code quality, stability, and maintainability. These issues might be tolerable in a prototype, but they can cause serious trouble when real people rely on the technology.

When vibe coding a rich text editor specifically, these issues show up fast. Documents may look fantastic in Chrome only to break in Firefox. Performance can stall on longer documents, causing difficult-to-reproduce, buggy behavior. Any number of problems can crop up once you’re past the demo phase. Remember that AI-written applications may impress stakeholders fast, but leave you with time-consuming maintenance issues and intensive reworks later.

#The hidden costs of not knowing the code

Of course, the main promise of AI is that you can develop software much faster. Developers can, in theory, create fully working features. But the output can include hallucinations, perform poorly, or open potential security holes (in fact, one study found that only 10.5% of code generated by an agent is secure). These issues can be hard to spot in a review without deep knowledge of the codebase by one of the creators.

Code is easiest to maintain when its intent, structure, and tradeoffs are deeply understood. That means building it yourself in-house or using a vendor that offers strong documentation and community support. By contrast, creating software using AI reduces expertise. A change in one spot could break functionality down the line. If no one has a strong understanding of the codebase, tracking down an issue could be a lengthy process.

Ultimately, creating a rich text editor requires years of specialized knowledge, including understanding document models, selection models, and serialization and deserialization. With AI-driven development, debugging code or making changes is hard from the start, and becomes much harder over time. This leads to compound risks and costs.

#The maintenance tax comes due

While AI development gets something working fast, you will rarely want to stick with your first draft. Features get added, each with the potential for inconsistencies or bugs. You will have to refactor down the line, costing you labor hours and preventing your team from working on higher-impact features.

For rich text editors, this maintenance debt compounds fast. Users discover edge cases and documents encounter backward-compatibility issues, requiring fixes. This slows development velocity and increases operational risk.

#Security and enterprise standards

AI-generated code doesn’t always get full security scrutiny. The AI output may be behind the times on best practices, since LLMs depend heavily on their training cutoff dates.

Your team will have to layer in changes to shore up your security posture. This is challenging since the team won’t know the codebase and must decipher intent line by line.

Rich text editors should be created with precision and security by design and default. They touch too many areas, such as embedded media, user-generated content, or pasted HTML, to leave to poor coding practices. For example, a user could intentionally or accidentally paste malicious code into the editor, leading to a security breach unless you’ve implemented robust input validation, content sanitization, and security controls throughout the editor. This doesn’t just apply to security: You also must develop compliant functionality and behavior. Regulations have quirks, and it requires expertise to ensure conformance. This can lead to extra review cycles, audits, and retrofits, or even compliance fines.

#When rich text breaks, it breaks in production

Rich text editors seem simple at first. But they are complex to build, and rarely work perfectly once deployed unless you’ve been working on them for a while. You can run into issues such as:

• Copy/paste issues from other document formats like HTML or Word.

• Browser quirks that make content render well in one and poorly in another.

• Selection issues such as cursors jumping around in long documents.

• Lists and tables not formatting correctly when pasted or resized.

• Text behaving oddly near media elements like videos or photos.

• State management issues that lead to saving errors.

Most of these edge cases emerge only after a demo has shipped to production. That makes rich text editors a poor fit for AI-driven coding approaches optimized for early success rather than long-term durability.

Every risk compounds in these editors over time. The components are too important to leave to an AI that hallucinates. For systems that function as core infrastructure, correctness, predictability, and long-term ownership matter far more than initial speed.

#Vibe coding vs owning a production system

The real issue isn’t who writes the code: it’s who owns the outcomes. Vibe coding optimizes for speed and visibility, while owning a production system focuses on supporting a system safely and cost-effectively for years.

The question is what you want to optimize for. AI-driven coding works fantastically for demos or internal, non-central apps. But if you’re facing a production environment with an important application, minimizing risk should be the goal. You’ll want methods that lead to:

• Predictable behaviors across browsers, devices, and edge cases.

• The ability to safely modify or add to the system.

• Strong security and compliance.

• Low maintenance costs.

These require more than vibes. They require deliberate design and meticulous coding principles.

#Speed without safety is just deferred risk

Rich text editors take time to develop, and even more time to maintain. Building one on your own rarely makes sense. Instead, it’s far better to start by buying a strong rich text editor component, then containing AI-assisted coding to lower-risk extensions.

CKEditor brings decades of experience developing its rich text editor component used in mission-critical applications across industries and businesses of all sizes. That means you don’t have to worry about edge cases that only surface after real users start editing real documents or about complex behavior breaking down at scale. You don’t have to chase browser-specific quirks, fragile cursor behavior, or formatting inconsistencies that only appear under regular use. You don’t have to retrofit security, compliance, or backward compatibility after the editor lives with real users.

That also means you won’t have to pay all the extra costs of long-term maintenance that come from vibe coding. Plus, it offers even more advanced features such as AI content creation tools for your end users, real-time and asynchronous collaboration tools, reliable rendering across devices, strong security, and much more.

Want to get your project up and running fast? Learn more by contacting the CKEditor team.