The Prototype Trap: Shipping AI-written Code Before It’s Ready

AI-assisted coding, otherwise known as “vibe coding,” enables development teams to build applications quickly. With just a descriptive prompt and a few minor tweaks, developers can produce code that works.

But there’s a catch: “working” doesn’t mean “production-ready.” In the haste to meet deadlines, organizations often push prototypes to production before they can weather the realities of enterprise usage. Pushing a prototype live will cost your organization over time.

Thankfully, there are fixes. This post will cover how the prototype trap gets set, what the big AI prototyping risks are, and how you can get out (or avoid it altogether). It will also explain why this is particularly complicated for rich text editors and WYSIWYG editors, as they’re the perfect example of a deceptively complex system that falls apart when a prototype faces real-world conditions.

#The prototype trap explained

The prototype trap occurs when a team develops a demo, proof of concept, or a minimum viable product. These are often built to prove functionality, explore feasibility, or to collect user feedback, but they end up pushed to production before they’re fully polished. The team must maintain the application long term, even if it lacked the proper quality controls and reviews.

This is particularly common now due to vibe coding. AI-assisted coding collapses the visual difference between a prototype and a production system. In short, the app looks good, so organizations feel it’s good enough to push.

This sets the trap. But the trap springs once real users rely on the application. Developers get bogged down with both maintenance and fixing bugs in a codebase they didn’t even create and don’t fully understand. That’s when costs rise, and deadlines get missed. Still, your team remains committed to the prototype due to sunk costs. In other words, you stepped on what looked like solid ground, but turned out to be quicksand.

Plus, some systems are more vulnerable than others. The trap is especially dangerous for complex components like rich text editors (RTEs), even if they seem simple at first glance. In fact, rich text editors or WYSIWYG editors offer a useful case study. At first, they look like a UI feature: Just a text box with some formatting instructions. But actually, they’re infrastructure that sits at the center of content workflows, compliance requirements, integrations, and user generated data. They look great in a demo, but rarely survive enterprise environments without significant hardening.

#Why some “simple” prototypes aren’t that simple

Some applications bring with them more risk than others. Some systems are cosmetic or simple, but others are critical infrastructure. Core infrastructure gets complex fast, fostering significant AI prototyping risks.

Rich text editors fall under core infrastructure. They manage structured content, preserve formatting across platforms, sanitize user input, and much more. They sit at the heart of business workflows like approvals, collaboration, and publishing. So not only are they complex, they’re so central that an error can hobble a good portion of the business.

In fact, people often bite off more than they can chew by developing it in house. Based on data from CKEditor’s State of Collaborative Editing 2025 report, only 2% of people building their own rich text editors didn’t face some sort of issue. The most commonly reported problems included development taking longer than expected (23%), higher costs than expected (21%), implementation being harder than anticipated (17%), engineers being pulled from other projects (14%), integration issues (13%), and an inability to meet the initial requirements (12%). It should be noted that all of these are more about the initial build, as maintenance wasn’t a top question asked.

The problem is they look self-contained in a demo. So it’s easy to fall into this trap, with stakeholders often thinking it’s more complete than it is. But in production, these WYSIWYG editors become a long-term dependency with complex edge cases and difficult maintenance demands. If you vibe code up front, the scaffolding won’t hold out.

#From prototype to production: where things go wrong

So why does the prototype trap happen? Here’s a common path:

• A problem gets discovered and engineering decides to build an app or a feature to address it.

• The team may hand code, but these days, odds are good they will vibe code some portion of their work.

• They show a demo that appears feature-rich. It works across platforms. The stakeholders like the output and want to get it launched soon.

• Developers make a few tweaks to get it “production-ready,” and then push what is still basically a prototype into production.

At this point, the code runs, the demo works, and the surface-level functionality looks solid. But the team doesn’t truly understand the system’s behavior, limits, and long-term AI prototyping risks.

Once this trap is set, organizational pressure takes over. The prototype has momentum, deadlines and expectations have been set, and stakeholders align around delivery. If you walk it back now, it looks like a delay, not responsibility. Pretty soon after, users start relying on the application, making it even harder to turn the car in reverse. This introduces more technical debt for the purposes of meeting near-term delivery, which just introduces risks down the line. But what are these risks?

#AI prototyping risks that leaders underestimate

Once a prototype goes into production, costs accumulate. Requirements grow, bugs appear, and vulnerabilities get discovered. Each issue raises the time and cost it takes to fix the next, compounding the costs over time.

The trap is particularly challenging for rich text editors. It’s tempting to vibe code something simple, since RTEs seem like just a contained UI component. But in practice, they’re core infrastructure. Once users depend on an editor, you’ll be battling to keep it running smoothly. That leads to a few major problems over time.

#Scope creep

Vibe prototyping is based on a snapshot of requirements at a particular point in time. This freezes the application before the full requirements list is understood.

For enterprise use, rich text editors almost always need more capabilities than imagined at the outset. These include:

• Real-time and asynchronous collaboration tools.

• Integrations across multiple internal systems and platforms.

• Reliable import and export with formats like Word and PDF, where formatting quirks are the norm.

• Granular user permissions for security and governance.

• Artificial intelligence and LLM connections to increase productivity. This brings its own complications, which you can read about in the eBook The 5% Solution for AI Content Creation.

Each new feature requires deep work to ensure it runs smoothly with the rest of the app and your tech ecosystem. In WYSIWYG editors in particular, each new feature or capability interacts with the underlying document model, so adding a new feature can sometimes require rework to avoid breaking formatting, selection behavior, rendering, or data integrity.

#Maintenance bill

Once the prototype reaches production, normal changes start to hurt. APIs evolve, browser updates introduce rendering issues, and integrations change. AI-driven apps are rarely created with those changes in mind, making each update require more effort than normal.

Plus, modifications in one area can introduce new issues elsewhere. With rich text editors, even small UI changes can have unpredictable second-order effects in selection behavior, formatting, and rendering. Over time, the maintenance costs more and siphons team capacity away from higher-value work.

#Knowledge gaps

One of the biggest AI prototyping risks is what happens when the original builder moves on. The system may continue to work, but the context behind design decisions disappears. Over time, fewer people understand how changes might ripple through the codebase.

As that understanding erodes, teams become cautious. Small updates feel risky, larger improvements get deferred, and the system slowly turns into something no one wants to touch. What started as a fast win becomes a fragile dependency.

This is especially true for editors. Much of the complexity is invisible: document transformations, schema rules, formatting issues, and cursor states, to name a few. Without deep knowledge of these internals, developers can be wary about even touching these systems.

#Security and compliance

Vibe-coded prototypes rarely get the level of security and compliance review that traditional software does, often shipping without a secure foundation.

In particular, rich text editors have a lot of traps for security and compliance teams. Security-wise, you must sanitize your user-created content to prevent malicious code insertions, build user permissions, and create strong logging and audit trails for security breaches and compliance reports. If you get this wrong, the WYSIWYG editor becomes a high-value target, making it a potential pipeline for untrusted content into your application (and the rest of your company infrastructure). Accessibility becomes its own beast as well, requiring deliberately designed and crafted UI components and compliant code for screen readers.

#Ongoing quality problems

There’s a broader problem with AI-coding and pushing prototypes early: speed becomes a proxy for team success. That pressure leads teams to sacrifice design thought and problem framing, reinforcing a culture of velocity over effectiveness. The result is a slow, hard-to-detect erosion of quality across the organization.

#Why “We’ll Fix It Later” Rarely Works

Why can’t you just address things at a later stage? Your team may have great intentions of iterating, but reality rarely matches up.

Being honest, most teams would admit that:

• Cleanup rarely gets prioritized. Teams move onto new projects, customers need new features, and employees move on to new jobs.

• Problems compound faster than you can fix them. Once that prototype hits production, unexpected issues pile up, and shifting dependencies force ongoing retrofits.

• Rewrites become harder over time. As people depend on the application, fixing it requires stopping or slowing operations and disrupting team workflows.

#How to break out of the AI prototype cycle

Once you recognize the trap, you can avoid it. This matters most for infrastructure-level components where shortcuts compound quickly. If you’re in leadership, you can steer the organization away from this risk. Here are three critical paths to sidestep the issue.

#1. Kill it early

Your best bet is to avoid the problem in the first place. If you’re in upper management, set expectations around demos and ask engineering very specific questions around safety and quality checks before anything goes live.

If you’re a product manager or developer, you’ll need to be adept at stakeholder management. Present all demos as a work in progress. Also, it helps to make it clear the work involved in getting something ready. You’ll want to control the organizational narrative before a leader or an executive gets excited and starts making promises you can’t keep.

#2. Build or rebuild it intentionally

If you’re already committed and want to stick with internal development teams only, you’ll need to do an intentional rebuild. Define the requirements up front, ensure the team designs the system for adaptability, and run it through all the checks prototypes normally skip: security, compliance, accessibility, performance, and maintainability.

#3. Buy a pre-built system, application, or component

Honestly, the easiest (and, in some cases, best) way is to just license from a vendor you trust. They’ve already done much of the hard work and continue to maintain and update the core components so you don’t have to.

For rich text editors in particular, it’s best to just start here or just swap out your current WYSIWYG editor if you built it in house rather than rebuilding it from scratch. Usually, for RTEs, you buy a base component, and integrate it wherever you need. This lets your team focus on application or product differentiation rather than trying to overcome solved problems every enterprise eventually hits.

When you choose this option, such as with CKEditor, you get:

• A mature infrastructure with a track record across many complex environments and enterprise setups.

• Ongoing updates and feature enhancements so you don’t have to spend dev time on keeping up. This includes the latest features in AI content creation so your end users can create documents faster.

• Security, compliance, and accessibility work that reduces overhead and long-term risk.

Buying an editor isn’t all-or-nothing either. Your team can still deeply customize it and add new features as needed. You buy the foundation, but can still tailor it to your needs.

#Questions leaders should ask before shipping an AI prototype

Before shipping any AI-generated prototype, leaders should be able to answer a few simple questions.

Who owns this code long-term?

If an update is required, you’ll have to designate developers responsible for fixing it. If it was vibe coded, the developer likely doesn’t know all of the code in detail, so assume it will take longer than you normally expect. They may be out of commission, working on other projects for a bit.

What happens when requirements grow?

Expect that your organization will demand more of the application over time. It’s important to plan for this and to have a process in place for capturing new requirements and building new features into the mix.

What assumptions did AI make?

Your team must be able to understand why AI made certain choices and how they were built. If they can’t, you’re shipping code you don’t understand, and that becomes a long-term problem.

What content or data flows are now locked in?

You need to know what data is being captured, where it’s going, and what logs, retention rules, and access controls now apply. If those answers are unclear, you’re shipping a compliance and security liability.

#The safer path forward

Prototypes have always snuck their way into production before they were ready. But with the advent of vibe coding, organizational pressure has exacerbated the problem. Valuing speed and early cost over quality will bite you later on.

Rich text editors are some of the worst applications to vibe code because, while they may look simple on the surface, they’re quite complex. It’s much better to build on top of a proven foundation from the start that already accounts for security, compliance, accessibility, and the ugly edge cases teams only discover in production.

If you’re serious about avoiding the prototype trap, the safest path is to start with a mature rich text editor component. CKEditor is built specifically for real-world conditions. If you want to see what that looks like for your company, contact our team.