Vibe Coding Technical Debt: Why It Happens and How to Prevent It

A working prototype can come together in an afternoon with AI writing most of the code, but the work required to keep it running smoothly is a lot harder to see at that stage. Vibe coding technical debt builds when teams extend or ship AI-generated code without enough understanding, testing, documentation, or ownership behind it.

This article covers what causes that debt, the warning signs to watch for, how to prevent it in new work, and how to address debt already baked into an existing application.

What Is Vibe Coding Technical Debt?

Vibe coding technical debt is the maintenance burden that builds up when repeated changes to AI-generated code work individually but don't fit together well, all made through prompts with limited direct examination of what the code actually does. Technical debt itself is nothing new. It's the cost of shortcuts and decisions that make future changes harder, whether or not anyone meant to create them. Vibe coding tends to generate that cost faster, since each prompt can look like it worked while the underlying pieces quietly drift apart. That's different from a defect or a security vulnerability, and the difference matters. A bug is simply wrong, while technical debt works today but costs more than it should to change tomorrow. An inadequate review can let both problems show up in the same feature.

Picture a customer portal that works great in a demo. Under the hood, its account rules are duplicated across three pages, and no one on the team is confident enough to touch any of them without breaking something else.

Why Does Vibe Coding Create Technical Debt?

Code generation from a prompt can move fast, but technical debt doesn't usually appear all at once. It builds up through a handful of recurring patterns, each with its own real cost down the line. In Sonar's 2026 developer survey, 40% of respondents said AI had increased technical debt by generating unnecessary or duplicate code.

• Limited application context. Generated code may miss existing business rules, integrations, or development conventions that a prompt never mentioned, and the AI has no way to know about.
• Inconsistent architecture. Repeated prompts and fixes can introduce different approaches to the same task across an application, so two features that should work the same way end up behaving differently.
• Duplicate logic and dependencies. New code may recreate existing functions or add packages without a clear need. That spreads the same logic across multiple files and quietly grows a codebase that's harder to reason about, or scale, with every addition.
• Missing tests. A feature can look complete without anyone checking failure paths, edge cases, or how it affects other features already running in production.
• Unclear ownership. Teams can struggle to explain why a change was made in the first place or who's actually responsible for maintaining it.

Picture a payments team using an AI coding tool like GitHub Copilot or a more autonomous setup with AI agents to speed through a new checkout flow. It's the same story at a lot of companies right now. Someone runs the same prompt a few times to get different pieces working, with the AI suggesting a slightly different approach each time. Each piece looks fine on its own. Input validation gets handled one way on the credit card form and a different way entirely on the gift card form. Error handling exists in some paths and not others. Hardcoded secrets slip into a config file because they worked during testing, and nobody circled back to remove them. None of this shows up in code review if nobody's looking closely. The existing test suite only checks the happy path, too, so test coverage on the failure cases stays close to zero.

What Are the Warning Signs of Vibe Coding Technical Debt?

Technical debt tends to announce itself eventually, usually through small frustrations that pile up faster than anyone expects. Whenever you implement AI-assisted coding, watch for:

• Small changes take a long time. A one-line fix ends up requiring hours of tracing and testing just to feel safe shipping it.
• The same business rule shows up more than once. Similar logic appears in multiple places with slightly different behavior, so fixing it in one spot doesn't actually fix it everywhere.
• Nobody can explain how something important works. Developers struggle to walk through an important workflow step-by-step when asked.
• Tests only cover the happy path. They pass for the successful case but miss errors, permissions, or unusual inputs that show up the moment real users get involved.
• Integrations behave inconsistently. Fixing one area quietly breaks another, and nobody notices until a user reports it.
• Dependencies keep piling up. A growing list of packages lacks a clear purpose or an owner who could explain why they are there.

In that customer portal example, this might look like an account rule that behaves differently depending on which page a customer is on, or a failed request handled one way on the billing page and a completely different way everywhere else.

When Does Vibe Coding Make Sense?

Vibe coding earns its reputation for a reason. For prototypes, concept testing, mockups, and disposable experiments meant to prove an idea rather than run in production, it can help a team keep moving fast, turning a rough concept into something people can actually click through in an afternoon. That sense of speed is reflected in Google Cloud's 2025 DORA research: more than 80% of surveyed technology professionals said AI had increased their productivity.

Software meant to stick around is a different story. An application headed for ongoing use needs review based on its actual data, users, integrations, and maintenance requirements, not just whether it works in a demo. None of this means professional developers have to avoid AI, or that using AI to write code requires a computer science degree to do so responsibly. A senior engineer can absolutely use AI coding tools for discrete tasks within an established software engineering practice without adopting a vibe-coding approach for the entire application.

For a broader comparison of vibe coding and low-code tools, see Liferay DXP's Vibe Coding vs. Low-Code Solutions guide.

How Can Teams Prevent Vibe Coding Technical Debt?

Preventing technical debt is less a technology challenge than a discipline one. It mostly comes down to treating the move from prototype to production as a real transition, not just a formality.

• Define the application's purpose and boundaries first. Nail down important business rules, data sources, and system boundaries before generating substantial code, so the AI has something real to work from.
• Check generated code against what already exists. Compare new changes with existing components and conventions to avoid quietly duplicating what's already there.
• Assign an owner to every production feature. Someone needs to actually understand it well enough to be the one called when it breaks.
• Review dependencies, authentication, and permissions. Check integration behavior too, since this is exactly where AI-generated code tends to miss things a prompt never asked about.
• Test critical user flows and failure paths. Require that those tests pass before any change is released, not just the tests that were convenient to write.
• Document the decisions a future maintainer will need. Someone else will have to understand this later, possibly without the context the original prompt writer had in mind.

Before calling anything production-ready, it's worth running through a short checklist:

• Can the team trace an important business rule back to where it actually lives in the code?
• Has anyone tested unauthorized access and failed integrations, not just the paths that are supposed to work?
• Is there a specific person who owns this feature and would know if it broke?
• Can the team detect a bad release quickly and recover from it without a scramble?

Answering yes to all four is a much better signal than a demo that happened to work.

How Can Teams Address Technical Debt in an Existing Vibe-Coded App?

Fixing tech debt in a live application requires a more careful approach than starting from a blank prompt.

Start by identifying the workflows users actually rely on and the areas responsible for recurring incidents or changes that take longer than they should. Map out the important dependencies, business rules, and integrations that are currently unclear to anyone on the team, including the parts nobody wants to touch. Add tests that capture how the application currently behaves before changing anything. That way, there's a way to know if a fix actually fixed anything or just moved the problem somewhere else.

From there, prioritize issues by their impact on users and the risk involved in touching them, rather than whatever feels most annoying that week. Consolidate duplicated logic and clean up inconsistent patterns in manageable steps instead of one sweeping rewrite. Add review, testing, and ownership requirements going forward, or the same debt just comes right back.

A full rewrite isn't usually the right default response. What makes sense depends on the application's current users, behavior, and condition. Sometimes the better move is incremental improvement rather than starting over.

Where Does Liferay DXP Fit in a Maintainable Development Strategy?

Vibe coding shines at getting an idea in front of people fast, but account management, request intake, approval routing, and access control are the kind of applications teams end up rebuilding the same pieces of in every custom-coded prototype. Liferay DXP turns those recurring pieces into ready-made, production-tested capabilities, so teams get the speed of vibe coding without having to write and retest the same source code from scratch every time.

Instead of prompting a new solution for each one, teams can configure:

• Objects to model structured data without custom schemas or database migrations
• Forms to collect information without hand-rolled validation logic
• Workflows to route approvals without wiring up a custom state machine
• Roles and permissions to control access without building authentication from scratch
• APIs and integrations to connect existing systems without custom connectors

When a requirement genuinely needs something custom, developers can still build components or client extensions on top of the platform, with the same review and testing standards applied to any other code. The advantage is choice. Teams decide where custom code actually earns its keep, while configurations, data models, workflows, and integrations run on standards and governance that are already built in rather than reinvented from scratch each time.

Make Fast Development Easier to Maintain

The value of a fast prototype isn't about how quickly it came together. It's about whether the team can fully understand it well enough to operate and change it safely six months later, long after the excitement of the first working version has worn off.

Most teams that ship faster only come out ahead if the productivity gain doesn't quietly turn into a bigger bill later, in the form of debugging time nobody budgeted for. As AI adoption grows, the best teams aren't the ones avoiding AI tools altogether. They're the ones being deliberate about which applications actually require custom code and which can run on established platform capabilities instead. Explore Liferay DXP's low-code capabilities to see where that balance might land for your team.

Frequently Asked Questions About Vibe Coding Technical Debt

Does vibe coding always create technical debt?

Not necessarily. It depends heavily on the application and on the review, testing, and maintenance practices surrounding the code generated. A weekend prototype that never touches real user data carries very different stakes than software supporting actual infrastructure that a business depends on.

Can code review alone prevent vibe coding technical debt?

Review helps, but it's not enough on its own. Unit tests, architectural decisions, documentation, and clear ownership all matter too. Code quality is about more than whether a reviewer approved a pull request without catching an obvious problem.

Should a vibe-coded application be rewritten if it has technical debt?

Not always, and definitely not by default. Incremental improvement is often the better call, especially once an application has real users depending on it. A full rewrite makes more sense when the existing code has become genuinely unworkable, over-engineered in some places, and missing basics in others, rather than just imperfect.

Can low-code applications accumulate technical debt?

Yes. Poorly planned workflows, data models, or integrations can create the same kind of debt without a single line of custom code involved. Low-code reduces some of the risk associated with software development from scratch. It doesn't remove the need for standards, ownership, and for someone to think through how the pieces fit together before they're built.