AI Code Fixing Guides

Learn how to identify, fix, and prevent common issues in AI-generated code. Transform ChatGPT prototypes into production-ready applications.

Why AI Code Needs Fixing

AI tools like ChatGPT, GitHub Copilot, and Claude are incredible for rapid prototyping. But they often generate code with:

Security vulnerabilities (hardcoded secrets, SQL injection risks)
Poor architecture (no separation of concerns, tight coupling)
Missing error handling (assumes happy paths only)
No testing strategy (works once, breaks later)
Performance issues (N+1 queries, memory leaks)

This guide helps you spot and fix these issues before they reach production.

Common AI Code Problems

1. Security Issues

Problem: AI often generates code with security flaws because it prioritizes “making it work” over “making it secure.”

Common Examples:

Hardcoded API keys and passwords
Missing input validation
SQL injection vulnerabilities
Exposed sensitive data in logs
CORS misconfiguration

How to Fix:

//  Bad (AI-generated)
const apiKey = "sk-1234567890abcdef";
const query = `SELECT * FROM users WHERE id = ${req.params.id}`;

//  Good (Production-ready)
const apiKey = process.env.API_KEY;
const query = `SELECT * FROM users WHERE id = ?`;
db.query(query, [req.params.id]);

Action Items:

Move all secrets to environment variables
Use parameterized queries or ORMs
Implement input validation and sanitization
Add authentication and authorization checks
Enable security headers (CORS, CSP, HSTS)

2. Error Handling

Problem: AI assumes everything works perfectly. Real applications need robust error handling.

Common Examples:

No try-catch blocks
Crashes on network failures
Silent failures
Poor error messages for users

How to Fix:

//  Bad (AI-generated)
async function fetchUser(id) {
  const response = await fetch(`/api/users/${id}`);
  return response.json();
}

//  Good (Production-ready)
async function fetchUser(id) {
  try {
    const response = await fetch(`/api/users/${id}`);

    if (!response.ok) {
      throw new Error(`HTTP ${response.status}: ${response.statusText}`);
    }

    return await response.json();
  } catch (error) {
    console.error("Failed to fetch user:", error);
    throw new Error("Unable to load user data. Please try again.");
  }
}

Action Items:

Wrap async operations in try-catch
Validate API responses before processing
Log errors for debugging
Show user-friendly error messages
Implement retry logic for transient failures

3. Performance Issues

Problem: AI writes code that “works” but doesn’t scale.

Common Examples:

N+1 database queries
Loading unnecessary data
No caching strategy
Synchronous operations blocking the main thread
Memory leaks from unclosed connections

How to Fix:

//  Bad (AI-generated) - N+1 Query Problem
async function getUsersWithPosts() {
  const users = await db.query("SELECT * FROM users");
  for (let user of users) {
    user.posts = await db.query("SELECT * FROM posts WHERE user_id = ?", [
      user.id,
    ]);
  }
  return users;
}

//  Good (Production-ready) - Single Query with JOIN
async function getUsersWithPosts() {
  const query = `
    SELECT u.*, p.id as post_id, p.title, p.content
    FROM users u
    LEFT JOIN posts p ON u.id = p.user_id
  `;
  const rows = await db.query(query);
  // Transform flat results into nested structure
  return transformToNestedStructure(rows);
}

Action Items:

Use database joins instead of multiple queries
Implement pagination for large datasets
Add caching for frequently accessed data
Use lazy loading for heavy resources
Profile and optimize bottlenecks

4. Code Organization

Problem: AI generates everything in one file with no structure.

Common Examples:

1000+ line files
Mixed concerns (UI, business logic, database)
No separation of routes, controllers, models
Duplicate code everywhere

How to Fix:

//  Bad Structure
app.js (2000 lines - everything in one file)

//  Good Structure
/src
  /routes
    user.routes.js
    auth.routes.js
  /controllers
    user.controller.js
    auth.controller.js
  /models
    user.model.js
  /middleware
    auth.middleware.js
    validation.middleware.js
  /services
    email.service.js
  /utils
    logger.js
  app.js

Action Items:

Separate concerns (MVC, Clean Architecture)
Create reusable utilities
Use dependency injection
Implement a clear folder structure
Extract business logic from routes

5. Missing Tests

Problem: AI doesn’t write tests. Your code works once, then breaks silently.

Common Examples:

No unit tests
No integration tests
No test data or fixtures
Untested edge cases

How to Fix:

//  Add Unit Tests
describe("User Service", () => {
  it("should create a new user with valid data", async () => {
    const userData = { name: "John", email: "[email protected]" };
    const user = await createUser(userData);

    expect(user).toBeDefined();
    expect(user.email).toBe("[email protected]");
  });

  it("should throw error for invalid email", async () => {
    const userData = { name: "John", email: "invalid-email" };

    await expect(createUser(userData)).rejects.toThrow("Invalid email");
  });
});

Action Items:

Write unit tests for business logic
Add integration tests for API endpoints
Test error cases and edge conditions
Set up CI/CD to run tests automatically
Aim for 70%+ code coverage

6. Environment Configuration

Problem: AI hardcodes values that should be configurable.

Common Examples:

Hardcoded database URLs
Fixed port numbers
Environment-specific logic mixed in code
No .env file support

How to Fix:

//  Bad (AI-generated)
const app = express();
app.listen(3000);
mongoose.connect("mongodb://localhost:27017/myapp");

//  Good (Production-ready)
require("dotenv").config();

const PORT = process.env.PORT || 3000;
const MONGO_URI = process.env.MONGO_URI || "mongodb://localhost:27017/myapp";

app.listen(PORT, () => {
  console.log(`Server running on port ${PORT}`);
});

mongoose.connect(MONGO_URI, {
  useNewUrlParser: true,
  useUnifiedTopology: true,
});

Action Items:

Use environment variables for all config
Create .env.example file
Document required environment variables
Use different configs for dev/staging/production
Never commit secrets to git

7. Database Issues

Problem: AI generates inefficient database schemas and queries.

Common Examples:

No indexes on frequently queried fields
Missing foreign key constraints
No data validation at database level
Poor normalization

How to Fix:

--  Bad (AI-generated)
CREATE TABLE users (
  id INT,
  email VARCHAR(255),
  name VARCHAR(255)
);

--  Good (Production-ready)
CREATE TABLE users (
  id INT PRIMARY KEY AUTO_INCREMENT,
  email VARCHAR(255) NOT NULL UNIQUE,
  name VARCHAR(255) NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
  INDEX idx_email (email)
);

Action Items:

Add proper constraints (NOT NULL, UNIQUE, FOREIGN KEY)
Create indexes on searched/filtered columns
Use transactions for multi-step operations
Implement soft deletes when needed
Use migrations for schema changes

8. API Design

Problem: AI creates inconsistent, poorly documented APIs.

Common Examples:

Inconsistent response formats
No API versioning
Missing status codes
No rate limiting

How to Fix:

//  Bad (AI-generated)
app.get("/user/:id", async (req, res) => {
  const user = await getUser(req.params.id);
  res.send(user);
});

//  Good (Production-ready)
app.get("/api/v1/users/:id", async (req, res) => {
  try {
    const user = await getUser(req.params.id);

    if (!user) {
      return res.status(404).json({
        success: false,
        error: "User not found",
      });
    }

    res.status(200).json({
      success: true,
      data: user,
    });
  } catch (error) {
    res.status(500).json({
      success: false,
      error: "Internal server error",
    });
  }
});

Action Items:

Use consistent response formats
Return appropriate HTTP status codes
Implement API versioning
Add rate limiting and throttling
Document endpoints with OpenAPI/Swagger

Quick Fixes Checklist

Use this checklist to audit AI-generated code:

Security:

No hardcoded secrets (use environment variables)
Input validation on all user inputs
Parameterized queries (no SQL injection)
Authentication and authorization implemented
Security headers configured

Error Handling:

Try-catch blocks around async operations
Validation of external API responses
User-friendly error messages
Proper error logging
Graceful degradation

Performance:

No N+1 query problems
Database indexes on queried fields
Caching for expensive operations
Pagination for large datasets
Connection pooling configured

Code Quality:

Proper folder structure
Separation of concerns
No code duplication
Meaningful variable names
Comments for complex logic

Testing:

Unit tests for business logic
Integration tests for APIs
Edge cases covered
CI/CD pipeline configured

Configuration:

Environment variables for config
.env.example file created
Different configs per environment
No secrets in version control

When to Get Professional Help

Some issues require experienced developers:

Complex architectural decisions
Large-scale refactoring
Performance optimization under load
Security audits for sensitive data
Migration to microservices
Setting up proper CI/CD pipelines

If you’re overwhelmed or need production-ready code fast, get in touch — we specialize in fixing AI-generated code.

Resources

Tools We Recommend:

ESLint/Prettier - Code quality and formatting
SonarQube - Static code analysis
OWASP ZAP - Security scanning
Jest/Mocha - Testing frameworks
Docker - Containerization
GitHub Actions - CI/CD

Further Reading:

Need Help?

Have AI-generated code that needs fixing? We specialize in transforming ChatGPT prototypes into production-ready applications.

Last updated: December 14, 2025

AI Code Fixing Guides

Content

AI Code Fixing Guides

Why AI Code Needs Fixing

Common AI Code Problems

1. Security Issues

2. Error Handling

3. Performance Issues

4. Code Organization

5. Missing Tests

6. Environment Configuration

7. Database Issues

8. API Design

Quick Fixes Checklist

When to Get Professional Help

Resources

Need Help?