How to Improve Code Quality During Vibe Coding

TL;DR

• AI-assisted coding does not automatically make code less maintainable, but the strongest current evidence says fast AI workflows need stronger guardrails.
• Write related tests in the same change as the feature, and use coverage to find gaps instead of chasing 100%.
• Keep AI diffs small so they are easier to review, easier to reason about, and less likely to introduce bugs.
• Turn on strict typing, linting, and formatting early so weak AI output gets caught before it lands in the codebase.
• Review AI output like any other code: check naming, responsibilities, duplication, edge cases, and error handling before you commit.

Vibe coding lets you ship at ridiculous speed. You describe what you want, the AI writes it, and suddenly you have a working app. The risky part is not AI-assisted coding itself. The risky part is assuming that speed automatically produces maintainable code.

The research is more nuanced than the hype. Controlled studies have found that AI-assisted code can be as maintainable as non-AI code in some tasks, and GitHub’s Copilot quality study reported modest improvements in readability and maintainability on a constrained exercise. At the same time, repo-scale analysis from GitClear found more duplicated code, more short-term churn, and less refactoring as AI usage increased. The takeaway is not “AI ruins code.” It’s that fast iteration needs stronger guardrails.

This doesn’t have to happen. You can vibe code and maintain quality. It just requires being intentional about a few habits.

The Real Cost of Skipping Quality

When you let an AI generate hundreds of lines without oversight, you accumulate what I call silent debt:

• Functions that do too many things and can’t be reused
• Duplicated logic scattered across files because the AI didn’t know about existing utilities
• No tests, so every change is a gamble
• Inconsistent naming that makes the codebase feel like it was written by five different people (because effectively, it was)

This debt compounds. Each new feature gets harder to add because you’re building on a shaky foundation. Eventually, the AI itself starts producing worse output because the context it reads is noisy and contradictory.

The fix is straightforward: treat quality as a continuous practice, not a cleanup phase.

Write Unit Tests as You Go

This is the single highest-leverage habit you can adopt during vibe coding. Not after. Not in a dedicated “testing sprint.” As you go.

Why Tests Matter Even More with AI

When you write code by hand, you have an implicit mental model of how it works. You tested it in your head while writing it. With AI-generated code, that mental model doesn’t exist — the AI had it, briefly, and then moved on.

Tests are the artifact that captures that understanding. They document what the code is supposed to do, and they catch it when it stops doing it.

How to Integrate Testing into Your Vibe Coding Flow

After the AI generates a feature, immediately prompt it to write tests:

• “Write unit tests for the calculateStreak function. Cover the happy path, edge cases for empty data, and timezone boundaries.”
• “Add tests for the API handler. Mock the database layer and test both success and error responses.”
• “Write integration tests for the checkout flow from cart to payment confirmation.”

Be specific about what to test. If you just say “write tests,” you’ll get shallow tests that assert obvious things. Push for edge cases, error paths, and boundary conditions. Keep the related tests in the same change as the production code whenever possible.

Structure Tests for Readability

Good tests follow the Arrange-Act-Assert pattern:

describe("calculateStreak", () => {
  it("returns zero when no completions exist", () => {
    const completions: Completion[] = [];

    const streak = calculateStreak(completions);

    expect(streak).toBe(0);
  });

  it("counts consecutive days ending today", () => {
    const completions = [
      { date: "2026-03-14", completed: true },
      { date: "2026-03-15", completed: true },
      { date: "2026-03-16", completed: true },
    ];

    const streak = calculateStreak(completions);

    expect(streak).toBe(3);
  });

  it("breaks streak on missed days", () => {
    const completions = [
      { date: "2026-03-13", completed: true },
      { date: "2026-03-15", completed: true },
      { date: "2026-03-16", completed: true },
    ];

    const streak = calculateStreak(completions);

    expect(streak).toBe(2);
  });
});

Each test reads like a specification. Months later, someone (or some AI) can look at these tests and understand exactly what calculateStreak is supposed to do.

Aim for Meaningful Coverage

Don’t chase 100% code coverage. Google’s testing guidance is explicit that maximizing the percentage alone can create a false sense of security and low-value tests. Use coverage to find gaps, not to declare victory. Instead, focus on:

• Business logic: calculations, transformations, state machines
• Edge cases: empty inputs, null values, boundary conditions
• Error handling: what happens when the network fails, the database is down, or the input is malformed
• Integration points: API contracts, database queries, third-party service interactions

Skip testing pure UI layout and framework boilerplate. That code changes too often and testing it adds friction without catching real bugs.

Apply Clean Code Patterns

AI-generated code tends to be functional but messy. It works, but it’s often verbose, poorly organized, and hard to modify. Clean code patterns fix this.

Single Responsibility

Every function and module should do one thing. When the AI generates a 60-line function that fetches data, transforms it, validates it, and renders it — that’s four responsibilities. Break it apart.

Prompt the AI to refactor: “Split this function into separate functions for fetching, transforming, validating, and rendering. Each function should have a clear name and return type.”

Meaningful Names

AI loves generic names: data, result, item, handleClick. These are almost always wrong. Names should reveal intent:

• data → userCompletions
• result → streakCount
• item → habitEntry
• handleClick → toggleHabitForDay

If you can’t name it clearly, you probably don’t understand what it does yet. That’s a signal to stop and think before continuing.

Keep Functions Small

A good rule of thumb: if a function doesn’t fit on your screen, it’s too long. Long functions are hard to understand, hard to test, and hard to reuse.

When the AI generates a large function, ask it to decompose: “This function is doing too much. Extract the validation logic into a validateHabitInput function and the persistence logic into a saveHabitEntry function.”

Eliminate Duplication

AI doesn’t have perfect recall of your entire codebase. It will often regenerate logic that already exists elsewhere. Watch for:

• Similar fetch wrappers in different files
• Repeated date formatting logic
• Duplicated validation rules
• Copy-pasted error handling patterns

When you spot duplication, consolidate it into a shared utility. Then tell the AI about it: “We have a formatDate utility in src/utils/date.ts. Use that instead of inline formatting.”

Use Types as Documentation

In TypeScript projects, types serve as living documentation. Instead of letting the AI use any or inline object shapes, define explicit interfaces:

interface Habit {
  id: string;
  name: string;
  frequency: "daily" | "weekly";
  createdAt: Date;
}

interface HabitCompletion {
  habitId: string;
  date: string;
  completed: boolean;
}

interface StreakResult {
  current: number;
  longest: number;
  lastCompletedDate: string | null;
}

These types make the AI’s future output better because it has clearer context about your data shapes. In TypeScript, strict checks like noImplicitAny and strictNullChecks also catch a class of errors at compile time instead of at runtime.

Establish Project Conventions Early

One of the biggest sources of messiness in vibe-coded projects is inconsistency. The AI writes each piece in isolation, so patterns drift.

Create a Project Structure

Before you start building, establish a clear folder structure:

src/
  components/     # Reusable UI components
  pages/          # Route-level components
  hooks/          # Custom React hooks
  utils/          # Pure utility functions
  services/       # API calls and external integrations
  types/          # TypeScript interfaces and types
  constants/      # App-wide constants and config
  __tests__/      # Test files (or colocate with source)

Tell the AI about this structure. When it creates a new file, make sure it goes in the right place.

Define Error Handling Patterns

Decide early how your app handles errors and be consistent:

class AppError extends Error {
  constructor(
    message: string,
    public code: string,
    public statusCode: number = 500
  ) {
    super(message);
    this.name = "AppError";
  }
}

function handleApiError(error: unknown): AppError {
  if (error instanceof AppError) return error;
  if (error instanceof Error) {
    return new AppError(error.message, "INTERNAL_ERROR");
  }
  return new AppError("An unexpected error occurred", "UNKNOWN_ERROR");
}

Once you have a pattern, tell the AI to follow it. Consistency in error handling prevents entire categories of bugs.

Use Linting and Formatting

Set up ESLint and Prettier before you start vibe coding. ESLint is built to identify patterns that make code inconsistent or bug-prone, and Prettier removes style debates by keeping the whole codebase on one formatting convention.

The AI will sometimes generate code that violates your lint rules. That’s fine — the linter catches it, and you fix it before committing. The point is to have a safety net.

Keep AI Changes Small

One of the easiest ways to protect quality is to ask the AI for one self-contained change at a time. Giant AI diffs are hard to reason about, hard to review, and easy to rubber-stamp.

Good prompts sound like this:

• “Only change the validation layer and its tests. Do not touch UI files.”
• “Implement the API endpoint in one PR-sized diff. Keep the React changes separate.”
• “Refactor this helper into a standalone utility without changing behavior.”

Google’s code review guidance is blunt here: small changes are reviewed faster, reviewed more thoroughly, and are less likely to introduce bugs. They should also include related test code.

Review AI Output Critically

The most important practice isn’t technical — it’s behavioral. Read what the AI writes. Don’t just check if it works. Check if it’s right.

If the change spans several files, start by understanding the shape of the diff. Sometimes it helps to read the tests first so you know the intended behavior before you inspect the implementation.

Questions to Ask During Review

• Does this function do only one thing?
• Are the names clear and intention-revealing?
• Are there edge cases that aren’t handled?
• Is there existing code that does something similar?
• Would I be comfortable debugging this at 2 AM?
• Can I explain what this code does without reading every line?

Red Flags to Watch For

• God functions: anything over 30-40 lines deserves a closer look
• Magic numbers and strings: hardcoded values that should be constants
• Swallowed errors: empty catch blocks or errors logged but not handled
• Implicit dependencies: functions that reach into global state or rely on side effects
• Missing null checks: AI often assumes happy-path inputs

When you spot these, don’t fix them by hand — ask the AI to fix them. This teaches it your standards for the rest of the session.

Refactor Regularly

Vibe coding creates code in bursts. After each burst, take a moment to clean up. This isn’t wasted time — it’s an investment that makes the next burst faster.

The Refactor Prompt Pattern

After completing a feature, run a refactoring pass:

• “Review the files we just created. Look for duplicated logic, overly complex functions, inconsistent naming, and missing types. Suggest refactoring improvements.”
• “Extract shared logic between HabitList and HabitDetail into a custom hook.”
• “The error handling in the API routes is inconsistent. Create a middleware pattern and apply it everywhere.”

When to Refactor

Refactor when you notice:

• You’re explaining to the AI what an existing function does (it should be obvious from the code)
• Adding a small feature requires touching many files
• You’re copying code between components
• Tests are breaking for unrelated changes

Don’t refactor for its own sake. Refactor when the current structure is actively slowing you down.

The Vibe Coding Quality Checklist

Before committing any vibe-coded feature, run through this checklist:

1. Tests exist for the business logic and edge cases
2. Functions are small and do one thing each
3. Names are meaningful — no data, result, temp, or handleClick
4. Types are explicit - avoid implicit any, turn on strict null checks, and prefer named shared types at important boundaries
5. Errors are handled — no empty catches, no swallowed failures
6. Duplication is minimal — shared logic lives in utilities or hooks
7. Linter passes — no warnings, no suppressions without comments explaining why
8. You can explain it — if you can’t describe what a piece of code does, rewrite it until you can

Quality Is Speed

It feels counterintuitive, but maintaining code quality during vibe coding actually makes you faster. Clean code is easier for the AI to understand and extend. Tests catch regressions before they compound. Consistent patterns mean less time explaining context in every prompt.

The developers who get the most out of vibe coding aren’t the ones who accept every AI output uncritically. They’re the ones who guide the AI toward clean, tested, well-structured code — and catch it when it drifts.

Vibe fast. But vibe clean.

Sources

1. Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability - Controlled study on whether AI-assisted code is harder for other developers to evolve.
2. Does GitHub Copilot improve code quality? Here’s what the data says - GitHub’s randomized controlled study on functionality, readability, maintainability, and approval rates.
3. AI Copilot Code Quality: 2025 Data Suggests 4x Growth in Code Clones - Repo-scale analysis showing more duplication, more short-term churn, and less refactoring in the AI era.
4. What to look for in a code review - Google’s guidance on reviewing tests, naming, design, and code health.
5. Small CLs - Google’s rationale for keeping changes small, self-contained, and shipped with related tests.
6. Navigating a CL in review - Review guidance that explicitly notes it can be helpful to read tests first.
7. Code Coverage Best Practices - Why coverage is useful as a signal, but a poor goal when chased for its own sake.
8. TypeScript TSConfig: noImplicitAny - Official docs on avoiding implicit any.
9. TypeScript TSConfig: strictNullChecks - Official docs on catching null and undefined issues before runtime.
10. Getting Started with ESLint - ESLint’s explanation of how linting improves consistency and helps avoid bugs.
11. Why Prettier? - Prettier’s explanation of why a shared formatting style reduces friction and review noise.