How to Stop AI from Generating Inconsistent Code

Monday: AI generates a function with try-catch error handling.

Tuesday: Same AI generates a similar function with .catch() chains.

Wednesday: Now it's using async/await with no error handling at all.

Your codebase is becoming a museum of coding styles.

Every AI generation introduces slight variations. Different patterns for the same problems. Your "consistent codebase" is now a patchwork.

Here's why this happens and how to stop it.

The Inconsistency Problem

What Inconsistent AI Code Looks Like

Error Handling:

TYPESCRIPT

// Version 1: try-catch
async function fetchUser(id: string) {
  try {
    const response = await api.get(`/users/${id}`);
    return response.data;
  } catch (error) {
    console.error('Failed to fetch user:', error);
    throw error;
  }
}

// Version 2: .catch()
function fetchUser(id: string) {
  return api.get(`/users/${id}`)
    .then(response => response.data)
    .catch(error => {
      console.log('Error:', error);
      return null;
    });
}

// Version 3: No handling
async function fetchUser(id: string) {
  const response = await api.get(`/users/${id}`);
  return response.data;
}

Naming:

TYPESCRIPT

// Version 1
const userData = await fetchUser(id);
const isUserValid = validateUser(userData);

// Version 2
const user_data = await fetch_user(id);
const user_is_valid = validate_user(user_data);

// Version 3
const u = await getUser(id);
const valid = check(u);

Component Patterns:

TSX

// Version 1: Arrow function with implicit return
const Button = ({ label }) => (
  <button className="btn">{label}</button>
);

// Version 2: Function declaration
function Button({ label }) {
  return (
    <button className="btn">{label}</button>
  );
}

// Version 3: Arrow with explicit return
const Button = ({ label }) => {
  return <button className="btn">{label}</button>;
};

All valid code. All doing the same thing. All slightly different.

Why AI Does This

1. Each request is independent

AI doesn't remember what it generated yesterday. Each interaction starts fresh, drawing from training data rather than your codebase history.

2. Training data contains all patterns

AI learned from millions of repositories. Some use try-catch, some use .catch(), some use neither. All patterns are "correct" to the AI.

3. Subtle prompt variations change output

Ask "create a function" vs "write a function" vs "add a function" and you might get different styles. AI is sensitive to phrasing.

4. No style enforcement mechanism

Nothing tells AI "this codebase uses Pattern A, not Pattern B." It makes fresh decisions each time.

The Real Impact

Code Review Burden

Reviewers now check for:

Does it work? ✓
Is it secure? ✓
Does it match our style? ← Hours of nitpicking

Every PR with AI code needs style corrections. The productivity gain from AI gets taxed by review overhead.

Onboarding Confusion

New developers look at your codebase and see:

Multiple ways to handle errors
Various naming conventions
Different component patterns

"Which pattern should I follow?" becomes an unanswerable question.

Maintenance Nightmare

Six months later, you need to update error handling across the codebase. But there are four different patterns to find and update. What should have been a simple find-replace becomes an archaeology project.

Developer Trust Erosion

Developers stop using AI for anything beyond throwaway code:

"I'll have to rewrite it anyway"
"It's faster to write it myself correctly"
"AI code always needs fixing"

You're not getting the value AI should provide.

Solutions That Don't Work

"Just be more specific in prompts"

You try adding style requirements to every prompt:

Create a function to fetch user data.
Use try-catch for error handling.
Use camelCase for variables.
Use async/await not promises.
Log errors with our logger.
Return null on failure, don't throw.

Problems:

Tedious to write every time
Easy to forget requirements
Different developers specify different things
Prompts become longer than the code

"Fix it in code review"

Accept AI inconsistency and fix during review.

Problems:

Defeats the purpose of AI productivity
Reviewers get fatigued
Some inconsistencies slip through
Technical debt accumulates

"Use linters and formatters"

ESLint and Prettier catch some issues.

Problems:

Only catches syntax-level patterns
Doesn't enforce architectural decisions
Can't specify "use this error handling pattern"
Doesn't affect what AI generates, only what gets committed

The Solution: Consistent AI Instructions

The fix is straightforward: give AI the same style instructions on every request.

What Consistent Instructions Look Like

Instead of hoping AI picks the right pattern, tell it explicitly:

MARKDOWN

# Code Style Requirements

## Error Handling
- Always use try-catch with async/await
- Log errors using the logger service, never console.log
- Include context in error logs: function name, parameters
- Return typed error objects, never null or undefined
- Always re-throw after logging for upstream handling

Example:
async function fetchUser(id: string): Promise<User> {
  try {
    const response = await api.get(`/users/${id}`);
    return response.data;
  } catch (error) {
    logger.error('fetchUser failed', { id, error });
    throw new ServiceError('Failed to fetch user', { cause: error });
  }
}

## Naming Conventions
- Variables: camelCase (userData, isValid, fetchCount)
- Constants: SCREAMING_SNAKE_CASE (MAX_RETRIES, API_TIMEOUT)
- Functions: camelCase, verb-first (fetchUser, validateInput, handleClick)
- Booleans: prefix with is/has/should (isLoading, hasError, shouldRetry)

## Function Style
- Use arrow functions for callbacks and simple utilities
- Use function declarations for top-level named functions
- Prefer explicit return statements over implicit returns
- Maximum function length: 30 lines

Making Instructions Automatic

The challenge: ensuring these instructions apply to every AI interaction, for every developer, every time.

Option 1: Copy-paste into prompts

Every developer pastes the style guide into every prompt.

Reality: Nobody does this consistently. Instructions get forgotten, abbreviated, or modified.

Option 2: Instruction files in repos

Add static instruction files (AGENTS.md, CLAUDE.md) to every repository.

Reality: Files drift across repos. Updates require touching every project. New repos start without them.

Option 3: SuperClawd (automatic enforcement)

Define style as skills that automatically apply to all AI interactions.

How it works:

Create a "Code Style" skill with your patterns
Add team members to workspace
Every AI interaction includes your style rules automatically

No copy-pasting. No files to sync. No hoping developers remember.

Building Effective Style Skills

Skill 1: Error Handling

MARKDOWN

# Error Handling Standards

## Pattern
Always use this error handling pattern:

async function serviceFunctionName(params: Type): Promise<Result> {
  try {
    // Operation
    return result;
  } catch (error) {
    logger.error('serviceFunctionName failed', { params, error });
    throw new ServiceError('User-friendly message', { cause: error });
  }
}

## Rules
- ALWAYS use try-catch with async/await
- NEVER use .catch() chains
- NEVER use console.log/error for logging
- ALWAYS include context in error logs
- ALWAYS use ServiceError class for service-layer errors
- ALWAYS re-throw for upstream handling

## Don't
- Don't return null to indicate errors
- Don't swallow errors silently
- Don't expose internal error messages to users

Skill 2: Naming Conventions

MARKDOWN

# Naming Conventions

## Variables and Functions
- camelCase for all variables: `userData`, `fetchCount`, `isLoading`
- camelCase for functions: `fetchUser`, `validateInput`, `handleSubmit`
- Verb-first for functions that perform actions: `get`, `fetch`, `create`, `update`, `delete`, `handle`, `validate`

## Booleans
Always prefix with:
- `is` for state: `isLoading`, `isValid`, `isAuthenticated`
- `has` for possession: `hasError`, `hasPermission`, `hasChildren`
- `should` for conditions: `shouldRetry`, `shouldRender`, `shouldFetch`

## Constants
- SCREAMING_SNAKE_CASE: `MAX_RETRIES`, `API_BASE_URL`, `DEFAULT_TIMEOUT`

## Types and Interfaces
- PascalCase: `User`, `ApiResponse`, `ButtonProps`
- Suffix interfaces with purpose when helpful: `UserService`, `AuthContext`, `ValidationResult`

## Files
- Components: PascalCase.tsx (`UserProfile.tsx`)
- Utilities: camelCase.ts (`formatDate.ts`)
- Types: camelCase.types.ts (`user.types.ts`)
- Tests: originalName.test.ts (`UserProfile.test.tsx`)

Skill 3: React Patterns

MARKDOWN

# React Component Standards

## Function Style
Use function declarations for components:

// Correct
function UserProfile({ user }: UserProfileProps) {
  return <div>{user.name}</div>;
}

// Incorrect
const UserProfile = ({ user }: UserProfileProps) => {
  return <div>{user.name}</div>;
};

## Props
- Always define props interface above component
- Name interface as ComponentNameProps
- Destructure props in function signature

interface UserProfileProps {
  user: User;
  onEdit?: () => void;
}

function UserProfile({ user, onEdit }: UserProfileProps) {
  // ...
}

## Hooks
- Call hooks at the top of the component
- Order: useState, useRef, useContext, custom hooks, useEffect
- Each useEffect should have a single responsibility

## Event Handlers
- Prefix with `handle`: `handleClick`, `handleSubmit`, `handleChange`
- Define inside component, not inline in JSX

Results: Before and After

Before SuperClawd

Developer A's AI generates:

TYPESCRIPT

const fetch_user = async (user_id) => {
  return api.get(`/users/${user_id}`).catch(e => null);
};

Developer B's AI generates:

TYPESCRIPT

async function getUser(id: string): Promise<User | undefined> {
  try {
    const res = await api.get(`/users/${id}`);
    return res.data;
  } catch (err) {
    console.error(err);
    return undefined;
  }
}

Same functionality. Completely different patterns.

After SuperClawd

Both developers' AI generates:

TYPESCRIPT

async function fetchUser(id: string): Promise<User> {
  try {
    const response = await api.get(`/users/${id}`);
    return response.data;
  } catch (error) {
    logger.error('fetchUser failed', { id, error });
    throw new ServiceError('Failed to fetch user', { cause: error });
  }
}

Same patterns. Every time. Every developer.

Implementation Path

Week 1: Document Your Patterns

Audit your codebase:

What error handling pattern do you use?
What naming conventions are standard?
What component patterns are established?

Don't invent new patterns—document what your best code already does.

Week 2: Create Skills

Convert documentation into SuperClawd skills:

One skill per category (errors, naming, components, etc.)
Include examples, not just rules
Specify what NOT to do

Week 3: Roll Out

Add team to workspace
Brief introduction (5 minutes)
Monitor first week for gaps

Ongoing: Iterate

Add patterns when inconsistencies appear
Update skills when patterns evolve
Review quarterly for relevance

Stop the Style Lottery

AI generating inconsistent code isn't inevitable. It happens because each AI interaction makes fresh decisions without knowing your established patterns.

The fix: Consistent instructions on every request.

The easy way: SuperClawd applies your style guide automatically, to every AI interaction, for every developer.

Your codebase stays consistent. Your code reviews focus on logic, not style. Your developers trust AI output.

Ready for consistent AI-generated code? Start with SuperClawd and define your code style once.

Use code WELCOME for free credits when you sign up!

The Inconsistency Problem

What Inconsistent AI Code Looks Like

Why AI Does This

The Real Impact

Code Review Burden

Onboarding Confusion

Maintenance Nightmare

Developer Trust Erosion

Solutions That Don't Work

"Just be more specific in prompts"

"Fix it in code review"

"Use linters and formatters"

The Solution: Consistent AI Instructions

What Consistent Instructions Look Like

Making Instructions Automatic

Option 1: Copy-paste into prompts

Option 2: Instruction files in repos

Option 3: SuperClawd (automatic enforcement)

Building Effective Style Skills

Skill 1: Error Handling

Skill 2: Naming Conventions

Skill 3: React Patterns

Results: Before and After

Before SuperClawd

After SuperClawd

Implementation Path

Week 1: Document Your Patterns

Week 2: Create Skills

Week 3: Roll Out

Ongoing: Iterate

Stop the Style Lottery

Turn your board into an autonomous pipeline