Keeping AI output maintainable

AI often produces code that works but is dense or hard to follow. With a few habits, you can steer the output toward something your team can maintain: clear names, small units, and tests that document behavior.

Why AI code can be hard to maintain

AI optimizes for producing correct, working code quickly. It does not optimize for:

• Your team's naming conventions
• Your project's folder structure
• Long-term readability
• Consistency with existing patterns

The result is often functional but dense code that works today but is hard to change tomorrow. Your job is to shape that output into maintainable code.

Ask for clear names and single responsibility

When you prompt, explicitly ask for descriptive names and focused functions:

In your prompt:

• "Use descriptive function names that explain what the function does"
• "Each function should do one thing"
• "Split this into a fetcher and a form component"
• "Name variables to reflect their content, not just their type"

If the first draft is a giant block, ask the AI to break it into smaller functions with clear names:

This function is too long. Split it into:
1. A function that validates input
2. A function that transforms the data
3. A function that handles the API call

Give each a descriptive name.

Prefer small, testable units

Large functions are hard to test and hard to change. Ask for logic in discrete steps:

In your prompt:

• "Extract the validation into a separate function"
• "Return a plain object so we can unit test it"
• "Separate the data transformation from the API call"

You can then test and refactor each piece without touching the rest.

Add or keep tests

Generated code without tests is a maintenance risk. When you request features, also request tests:

In your prompt:

• "Also write a test file for this function"
• "Include tests for the happy path and these edge cases: [list]"
• "Use our testing patterns with Jest and React Testing Library"

Run the generated tests. Extend them for edge cases the AI missed. Tests document how the code is supposed to behave when you or someone else changes it later.

Minimum test coverage for AI code

Align with your project structure

AI does not know your project structure. Generated code may:

• Use different folder conventions
• Handle errors differently
• Use different import patterns
• Follow different naming conventions

Your job: Move code to the right place and refactor so it fits:

AI output goes in: utils/helpers.ts
Your convention is: lib/utils/stringUtils.ts

Move it and update imports.

Consistency makes everything easier to maintain. Do not let AI code create islands of different patterns.

Checklist for alignment

• File is in the correct folder
• Names match project conventions
• Error handling matches existing patterns
• Imports use your path aliases (@/ or relative as appropriate)
• Logging uses your logger, not console
• Types are in the right place

Document the tricky parts

If the AI used a non-obvious approach, add a short comment:

Good comments:

// Using a WeakMap here to avoid memory leaks with DOM references
// Regex handles edge case where input contains escaped quotes
// setTimeout(0) ensures the DOM has updated before measuring

Unnecessary comments:

// Loop through the array
// Return the result
// Call the function

Document the "why," not the "what." Future you (or a teammate) will thank you.

Common maintainability issues in AI code

Practical workflow

When you receive AI-generated code:

1. Review for correctness: Does it do what you asked?
2. Check names: Are function and variable names clear?
3. Check size: Are functions small and focused?
4. Check structure: Does it fit your project patterns?
5. Run tests: Do existing tests pass?
6. Add tests: Does the new code have test coverage?
7. Refactor: Make any changes needed for maintainability
8. Commit: Only after it meets your standards

Example: reshaping AI output

AI output (works but dense):

function handleSubmit(data: any) {
  if (!data.email || !data.email.includes('@')) return { error: 'Invalid email' };
  if (!data.password || data.password.length < 8) return { error: 'Password too short' };
  const user = { email: data.email, password: hashPassword(data.password), createdAt: new Date() };
  return saveUser(user).then(result => ({ success: true, user: result })).catch(e => ({ error: e.message }));
}

After reshaping for maintainability:

interface SignupData {
  email: string;
  password: string;
}

interface SignupResult {
  success: boolean;
  user?: User;
  error?: string;
}

function validateEmail(email: string): string | null {
  if (!email || !email.includes('@')) {
    return 'Invalid email address';
  }
  return null;
}

function validatePassword(password: string): string | null {
  if (!password || password.length < 8) {
    return 'Password must be at least 8 characters';
  }
  return null;
}

function createUserObject(data: SignupData): NewUser {
  return {
    email: data.email,
    password: hashPassword(data.password),
    createdAt: new Date(),
  };
}

async function handleSignup(data: SignupData): Promise<SignupResult> {
  const emailError = validateEmail(data.email);
  if (emailError) return { success: false, error: emailError };

  const passwordError = validatePassword(data.password);
  if (passwordError) return { success: false, error: passwordError };

  try {
    const user = await saveUser(createUserObject(data));
    return { success: true, user };
  } catch (error) {
    return { success: false, error: getErrorMessage(error) };
  }
}

The reshaped version:

• Has typed inputs and outputs
• Splits validation into testable functions
• Separates concerns (validation, object creation, persistence)
• Uses async/await for clarity
• Handles errors consistently

Summary

AI generates working code quickly. Making it maintainable is your job:

1. Ask for clear names and single responsibility in your prompts
2. Request small, testable units instead of large functions
3. Add or keep tests to document behavior
4. Align with your project structure and patterns
5. Document tricky parts with short comments

For more on reviewing AI output before it lands in the repo, see Reviewing AI-generated code. For refactoring patterns, see Using AI for refactoring.