AI-generated code is only as clean as the process around it.
The "AI writes spaghetti code" narrative is everywhere: on Reddit, in conference talks, in the minds of CTOs who've never actually measured it. And they're not entirely wrong. Naive use of AI tools does produce poor code. But the fault lies in the process, not the technology. With the right techniques, AI can match human code quality and in places exceed it. Here's how we approach it.
The Reality: Quality Depends on Process
Garbage in, garbage out. That rule hasn't changed since 1957.
Give AI a vague prompt like "write a function to process user data," and you'll get vague, untestable code. Give it typed interfaces, validation patterns, error handling conventions, and explicit project context? You'll get clean, maintainable implementations that match your codebase like they were written by a senior developer who's been on the team for years.
Our process has evolved through hands-on AI-assisted development. The techniques below represent hard-won lessons. These same techniques power our AI-first development methodology.
How We Ensure Quality
Strategic Prompting Techniques
The prompt is everything. A 30-second investment in prompt quality saves hours of refactoring.
Bad prompt:
"Write a function to process user data"
Good prompt:
"Write a TypeScript function that processes user profile data. Requirements:
- Accept a UserProfile object with fields: id (string), name (string), email (string), createdAt (Date)
- Validate that email matches standard email format
- Return a ProcessedProfile with sanitized name (trimmed, title-cased) and validated email
- Throw a ValidationError with descriptive message if validation fails
- Follow existing project patterns in src/utils/validation.ts
- Include JSDoc comments
- Aim for pure function without side effects"
The detailed prompt constrains the AI to produce code that:
Test-Driven Development with AI
TDD and AI are a natural pair. The feedback loop creates a self-correcting system:
- Write the test first (or have AI generate tests from requirements)
- Generate implementation targeting the test
- Run tests and provide failure feedback to AI
- Iterate until tests pass
Bugs die in seconds, not days. If the AI generates code that doesn't meet requirements, the tests fail, and the AI self-corrects before you ever see the broken version.
AI often iterates through a few versions before producing a clean solution that passes all tests. Sound familiar? Human developers do the same thing. They just do it slower. For more on our testing approach, see AI-assisted software testing.
Automated Linting and Formatting
All AI-generated code passes through the same automated quality checks as human code:
- •ESLint/TypeScript for static analysis and type checking
- •Prettier for consistent formatting
- •Custom rules for project-specific conventions
Code that doesn't pass gets rejected and regenerated automatically. Zero tolerance. Zero exceptions. This ensures baseline quality regardless of whether a human or AI wrote it.
The Code Review Process
AI-generated code never hits production without human eyes. Every line is treated as a draft requiring validation. For a detailed guide, see AI Code Review: What Human Reviewers Should Look For.
AI-Generated Code as "Draft"
Think of AI output as a highly skilled junior developer's first attempt. Often good. Sometimes excellent. Occasionally needs a firm redirect. Your job is to:
- Verify correctness: Does it actually solve the problem?
- Check edge cases: Are boundary conditions handled?
- Evaluate fit: Does it match project patterns?
- Assess readability: Would a new team member understand it?
Human Curation and Refinement
Common refinements we make to AI-generated code:
Naming improvements: AI sometimes uses generic names. We rename for clarity and consistency with project conventions.
Simplification: AI occasionally over-engineers. We simplify when simpler solutions work.
Pattern alignment: AI doesn't always match project-specific patterns. We adjust to maintain consistency.
Edge case handling: AI might miss obscure edge cases. We add handling for cases our domain knowledge identifies.
Naming Conventions Enforcement
Clear naming is crucial for maintainability. We enforce:
- •Functions: verb + noun (getUserById, validateEmailFormat)
- •Variables: descriptive nouns (userProfile, validationErrors)
- •Constants: SCREAMING_SNAKE_CASE
- •Types/Interfaces: PascalCase with descriptive names
When AI uses generic names like "data" or "result," we rename to be specific. "processedUserProfile" is always clearer than "processedData."
Common AI Code Pitfalls (and How We Avoid Them)
We've catalogued the common failure modes. Every one is preventable:
Generic Naming
The problem: AI defaults to generic names like "data," "result," "item," "handler."
Our solution: We explicitly request domain-specific naming in every prompt and rename during review. After providing 3-5 examples from your codebase, the AI adapts to your conventions automatically.
Over-Complexity
The problem: AI sometimes produces overly abstracted or complex solutions when simpler ones work.
Our solution: We include "simple, straightforward implementation" in every prompt by default. During review, we apply one test: could this be simpler? If yes, regenerate. Simplicity is a feature, not a compromise.
Missing Edge Cases
The problem: AI handles the happy path well but may miss edge cases.
Our solution: We enumerate edge cases in every prompt: null inputs, empty arrays, network failures, concurrent access. Comprehensive test suites covering boundary conditions catch what we miss.
Inconsistent Error Handling
The problem: Error handling style varies across AI-generated code.
Our solution: One sentence changes everything: "Follow the error handling pattern in src/utils/errors.ts." Combined with custom ESLint rules that enforce our patterns, error handling consistency went from our biggest AI complaint to a non-issue within two weeks.
Real Examples: Before and After
Example 1: Data Validation
Raw AI output:
function validate(data: any) {
if (!data.email) return false;
if (!data.name) return false;
return true;
}
After refinement:
interface UserInput {
email: string;
name: string;
}
interface ValidationResult {
isValid: boolean;
errors: string[];
}
function validateUserInput(input: UserInput): ValidationResult {
const errors: string[] = [];
if (!input.email?.trim()) {
errors.push('Email is required');
} else if (!isValidEmailFormat(input.email)) {
errors.push('Email format is invalid');
}
if (!input.name?.trim()) {
errors.push('Name is required');
} else if (input.name.length < 2) {
errors.push('Name must be at least 2 characters');
}
return {
isValid: errors.length === 0,
errors,
};
}
The refined version adds proper types, specific validation rules, and actionable error messages. Four minutes of refinement. Four hours of debugging saved downstream.
Example 2: API Handler
Raw AI output:
async function handler(req, res) {
const user = await db.users.findById(req.params.id);
res.json(user);
}
After refinement:
async function getUserHandler(
req: Request<{ id: string }>,
res: Response<UserResponse | ErrorResponse>
): Promise<void> {
const { id } = req.params;
if (!isValidUuid(id)) {
res.status(400).json({ error: 'Invalid user ID format' });
return;
}
const user = await userRepository.findById(id);
if (!user) {
res.status(404).json({ error: 'User not found' });
return;
}
res.json(mapToUserResponse(user));
}
Typing, validation, error handling, proper response mapping: all added in a short review. The raw version was a production incident waiting to happen.
Metrics: What to Track
We do not publish bug-rate comparisons, because they depend on the codebase, the team and how bugs are counted. Track these on your own code, for AI-generated and human-written changes alike.
Bug Rates
Compare bugs per thousand lines, and critical bugs, across three groups: human-written code, unrefined AI output, and AI output that went through your review process. Process is the variable, not the tool. The same holds for the tooling itself: the harness around the model accounts for more of the outcome than the model choice does.
Maintainability Scores
Use an industry-standard maintainability index (higher is better) and compare the same three groups.
The key factor: consistency. AI with proper prompting doesn't have bad days, doesn't cut corners on Friday afternoons, and doesn't skip documentation because it's tired.
Test Coverage
Compare average coverage for human-written and AI-generated tests. Coverage alone is not quality, but it separates "probably works" from "definitely works" when the tests assert real behavior.
Frequently Asked Questions
How long does it take to refine AI-generated code?
Usually a fraction of the time it would take to write from scratch. Refinement is quick when the prompt carried the project context, and slow when it did not.
Does this process work for all languages?
Yes, though results vary by language. TypeScript, Python, and Java produce the highest quality output due to larger training datasets. Niche languages like Elixir or Zig need more refinement time.
How do you handle code that needs to integrate with existing systems?
Context is king. We provide existing patterns, interface definitions, and explicit file references as examples. The instruction "match the style of src/services/userService.ts" produces remarkably consistent integrations. More context always means better output.
What about security vulnerabilities in AI-generated code?
Every line (human or AI) runs through automated security scanning. We also include OWASP Top 10 awareness in our standard prompts.
Building Quality Into Your AI Workflow
Clean AI code isn't magic. It's discipline.
The investment in building a proper AI development process pays for itself and compounds from there. If you're struggling with AI code quality, the problem is almost certainly your prompts and review process, not the AI. Fix the process. The quality follows.
Contact us to talk through how AI-written code should be reviewed in your team.