Vibe Coding and Technical Debt: What Website Teams Need to Know

What Does Vibe Coding Mean?

People use the term in different ways. In the narrower definition discussed by Martin Fowler and Simon Willison, vibe coding means directing an AI model through prompts and trying the result while doing little or no review of the generated code. In broader everyday use, it can also refer to heavily AI-assisted development.

Those meanings describe different levels of oversight. A developer can have AI generate substantial code while still reviewing the changes, understanding the design, and verifying behaviour. For a project discussion, describe the actual workflow: what was generated, what was checked, and who maintains it.

Exploratory work can be useful for testing an idea or comparing interfaces before investing further. When a prototype becomes a business website, the team needs to reassess its responsibilities and operating conditions.

Technical Debt Is the Cost of Making Future Changes

Technical debt is a metaphor for internal design or implementation problems that make later work harder. Fowler describes the additional effort as interest on the debt. Improving the underlying design is comparable to repaying the principal.

Consider a hypothetical promotional banner copied into twelve templates. The first release may be straightforward. A later wording change now requires finding twelve copies, updating them consistently, and checking every location. The repeated effort reveals the maintenance cost.

Debt can be intentional or discovered later. A temporary campaign may justify a limited shortcut, while a feature used across a growing site may benefit from a reusable design. Learning that an earlier design no longer fits the product is also a normal part of development.

Where Website Maintenance Problems Can Appear

The following examples are prompts for investigation. A pattern becomes a concern when it creates measurable difficulty, incorrect behaviour, or an avoidable dependency. A large file, an older framework, or an individual CSS override does not establish that a project is poorly built.

A Practical Example: A Form That Submits Twice

Imagine a contact form inside a modal. Each time the modal opens, an initialisation function attaches a fresh submit handler. The first submission behaves normally. After repeated openings, several handlers may process the same submission and create duplicate requests.

A symptom-focused patch might disable the submit button sooner or hide one confirmation message. The underlying question is whether the form’s event lifecycle is correct: when does setup run, can it run repeatedly, and what removes or prevents duplicate listeners?

A focused investigation would record the steps, inspect network requests, identify the code responsible for initialisation, and verify the intended behaviour after the fix. The acceptance condition could be one request per valid submission, including after repeatedly opening and closing the modal.

This is a hypothetical example, not a diagnosis of a particular website. Similar reasoning applies to cart interactions, filtering controls, and components re-rendered by a theme or framework.

Why Teams Need Shared Understanding

A feature can become difficult to maintain when the explanation for its behaviour exists only in one person’s memory or an AI chat. The next developer may see the implementation without knowing which requirements, failed approaches, or integration constraints led to it.

Useful handover information belongs with the project. Record which module owns the feature, how to run it, which external systems it uses, how to verify it, and which limitations are accepted. Document configuration names and the approved way to obtain access without placing secret values in the repository.

Responsibility should be explicit. Someone needs to make the implementation decision, someone needs to review consequential changes, and someone needs to maintain the released feature. In a small team, one person may hold several roles. The important point is that the responsibilities are understood.

AI can help explain an unfamiliar component or draft documentation. A maintainer should check that explanation against the code and observed behaviour before relying on it.

What Research Can and Cannot Tell Us

Research findings depend on the task and environment. GitHub reported improved test outcomes and reviewer assessments in a controlled study of API coding tasks with 202 valid developer submissions. This was vendor-published research about a specific exercise, rather than a long-term audit of website maintenance.

METR’s early-2025 trial involved 16 experienced open-source developers completing 246 tasks in familiar repositories and found that AI access increased completion time by 19%. Its February 2026 update said later measurements were affected by selection and measurement problems, making the current effect difficult to estimate reliably.

These studies examine different workflows and outcomes. They do not directly establish whether an unreviewed website will remain maintainable over several years. A team should evaluate its own delivery time, review effort, defects, and maintenance work rather than treating one percentage as a universal forecast.

Review the Website Through Its Actual Responsibilities

A useful review follows the work the website performs. A brochure page, an online store, and a customer account portal have different failure consequences. Agree on the required checks before treating a prototype as a released feature.

The table below is a practical starting point, not a security certification or an exhaustive test plan. Choose checks according to the platform, change, and information involved.

Keep Code Review Focused and Reproducible

Google’s engineering guidance recommends small, self-contained changes that reviewers can understand in context. A change should include the information and related verification needed to assess it. There is no universal line-count threshold that makes a pull request good.

For website work, separate a navigation fix from an unrelated animation redesign or dependency upgrade where practical. A reviewer can then identify the intended behaviour, inspect the relevant diff, and assess the effect with less unrelated context.

AI review can provide another perspective, but its output also needs evaluation. GitHub’s documentation identifies limitations in AI code review and recommends supplementing it with careful human review. A generated test can be useful, provided its assertions actually represent the requirement.

For the hypothetical modal form, a useful regression check reproduces repeated opening and verifies the resulting request count. A screenshot of a success message alone would leave the original failure untested.

Discuss Maintenance Concerns Without Blame

Team discussions work better when they describe an observable behaviour, its effect, and a proposed next step. Google’s guidance on review comments recommends courtesy, reasoning, and comments directed at the code. It also recommends distinguishing required changes from optional suggestions.

In a hypothetical review, a useful observation might be: opening the form three times produces three submit requests in the staging environment. The impact is duplicate lead records. The proposed next step is to review initialisation and confirm that one valid submission produces one request.

That description gives the team something to reproduce and resolve. A disagreement about naming or formatting can be handled through project conventions, while a demonstrated functional problem gets an acceptance condition.

Use the same evidence standard for generated and manually written code. Review the consequences of the implementation and the quality of the verification. A person’s job title or preferred tool does not explain why a particular behaviour occurs.

Prioritise Debt by Its Consequences

A bug is incorrect behaviour. Technical debt concerns the difficulty of changing the implementation. A security vulnerability is a weakness that needs assessment according to its potential harm and exposure. These can overlap, but each needs a clear response.

Start with problems affecting sensitive data, important business actions, or reliable operation. For maintenance improvements, examine how often the area changes, how much extra effort it causes, and the risk of the proposed cleanup. A rarely changed component may be a lower priority than a shared module that repeatedly causes regressions.

Fowler describes gradual improvement in frequently changed areas as a practical way to reduce maintenance cost. Refactoring means changing internal structure while preserving intended behaviour, so the team still needs evidence that the relevant behaviour remains intact.

Observation: repeated modal openings attach multiple submit handlers.
Impact: one user action can create duplicate lead requests.
Proposed change: give form initialisation one clear lifecycle owner.
Verification: repeat open/close cycles, then confirm one valid request.
Owner: maintainer responsible for the form integration.
Priority: resolve before expanding the same form to more templates.

A Practical Handover Checklist

Before another person takes responsibility for a generated website or feature, leave enough information to reproduce and evaluate it. The checklist should be proportionate to the project and remain current as the implementation changes.

Use AI With a Clear Maintenance Plan

AI can support prototyping, code explanation, test drafting, and implementation. The appropriate level of review depends on what the software does and what can happen when it fails.

For a team, a useful outcome is a website whose behaviour can be verified and whose implementation can be changed by its maintainers. Describe that outcome in requirements and handover notes, then use whichever tools help the team reach it.

A prototype can remain a useful experiment. A production feature needs an understood path for operation, changes, and recovery. Making that decision explicit helps the team estimate the remaining work without assuming that every AI-generated project needs a rebuild.