How to Use AI for Test-Driven Development

Test-Driven Development has always had a marketing problem. Everyone agrees it’s a good idea. Almost nobody does it consistently. The reason is simple: writing tests first is mentally taxing. You have to think about behavior before implementation, define edge cases upfront, and resist the urge to “just write the code real quick.”

AI changes the economics of TDD completely. Here’s how I’ve integrated AI into a test-first workflow that I actually stick with.

A Quick TDD Refresher

The classic TDD cycle is Red-Green-Refactor. Write a failing test (red). Write the minimum code to pass it (green). Clean up the code (refactor). Repeat. The idea is that tests drive the design of your code rather than being bolted on afterward.

The problem with traditional TDD is Step 1. Writing good tests requires you to think through the API surface, edge cases, and error scenarios before you’ve written any implementation. That’s valuable thinking, but it’s slow and exhausting.

The AI-TDD Workflow

Here’s my updated workflow: I describe the behavior I want, AI writes the tests, I review and adjust them, then AI generates the implementation. Let me show you a real example.

Step 1: Describe the Behavior

I need a function called formatCurrency that:
- Takes a number and a currency code (USD, EUR, GBP)
- Returns a formatted string like "$1,234.56"
- Handles negative numbers with parentheses: ($1,234.56)
- Rounds to 2 decimal places
- Throws on invalid currency codes
- Handles zero, very large numbers, and very small decimals

Write comprehensive Vitest tests for this function.
Don't write the implementation yet.

Step 2: AI Generates Tests

import { describe, it, expect } from 'vitest';
import { formatCurrency } from './format-currency';

describe('formatCurrency', () => {
  it('formats basic USD amount', () => {
    expect(formatCurrency(1234.56, 'USD')).toBe('$1,234.56');
  });

  it('formats EUR with euro symbol', () => {
    expect(formatCurrency(1234.56, 'EUR')).toBe('u20AC1,234.56');
  });

  it('formats GBP with pound symbol', () => {
    expect(formatCurrency(1234.56, 'GBP')).toBe('u00A31,234.56');
  });

  it('wraps negative numbers in parentheses', () => {
    expect(formatCurrency(-1234.56, 'USD')).toBe('($1,234.56)');
  });

  it('rounds to 2 decimal places', () => {
    expect(formatCurrency(1234.5678, 'USD')).toBe('$1,234.57');
  });

  it('handles zero', () => {
    expect(formatCurrency(0, 'USD')).toBe('$0.00');
  });

  it('handles very large numbers', () => {
    expect(formatCurrency(1000000000, 'USD')).toBe('$1,000,000,000.00');
  });

  it('handles small decimals', () => {
    expect(formatCurrency(0.001, 'USD')).toBe('$0.00');
  });

  it('throws on invalid currency code', () => {
    expect(() => formatCurrency(100, 'XYZ')).toThrow('Unsupported currency: XYZ');
  });

  it('handles negative zero', () => {
    expect(formatCurrency(-0, 'USD')).toBe('$0.00');
  });
});

I review these tests carefully. This is the critical human step. I notice the AI included a negative zero test I wouldn’t have thought of. I also add a test for NaN input that it missed. The review takes two minutes, but it shapes the entire implementation.

Step 3: Run the Tests (Red)

All tests fail because the function doesn’t exist yet. Good. That’s the point.

Step 4: Generate the Implementation

Here are my Vitest tests for formatCurrency: [paste tests]

Now write the implementation that passes all these tests.
Use TypeScript. No external dependencies.

The AI produces an implementation. I run the tests. If any fail, I feed the failure back to the AI. Usually one or two iterations gets everything green.

Step 5: Refactor

With all tests passing, I refactor the implementation with confidence. The tests are my safety net. I might simplify the AI’s implementation, extract constants, or improve readability. Tests stay green throughout.

AI Suggests Edge Cases You Miss

One of the biggest benefits of AI-TDD is edge case discovery. When I ask AI to write tests, it consistently suggests cases I wouldn’t have considered:

• Negative zero handling
• Unicode edge cases in string processing
• Concurrent access patterns in async code
• Boundary values (MAX_SAFE_INTEGER, empty arrays, null prototypes)

Not every suggestion is relevant, but the ones that are catch real bugs before they reach production.

Component Testing with AI-TDD

This workflow works just as well for React components. Describe the component behavior, get tests, then build the component:

describe('SearchInput', () => {
  it('renders with placeholder text', () => {
    render(<SearchInput placeholder="Search products..." />);
    expect(screen.getByPlaceholderText('Search products...')).toBeInTheDocument();
  });

  it('debounces onChange by 300ms', async () => {
    const onChange = vi.fn();
    render(<SearchInput onChange={onChange} />);

    await userEvent.type(screen.getByRole('textbox'), 'test');
    expect(onChange).not.toHaveBeenCalled();

    await vi.advanceTimersByTimeAsync(300);
    expect(onChange).toHaveBeenCalledWith('test');
  });

  it('shows clear button when input has value', async () => {
    render(<SearchInput />);
    expect(screen.queryByRole('button', { name: /clear/i })).not.toBeInTheDocument();

    await userEvent.type(screen.getByRole('textbox'), 'test');
    expect(screen.getByRole('button', { name: /clear/i })).toBeInTheDocument();
  });
});

These tests define the component contract before a single line of JSX exists. When you hand this to AI for implementation, the output is focused and constrained by the tests rather than by guesswork.

Why AI Makes TDD Stick

Traditional TDD fails because the upfront cost is high. AI reduces that cost by 70-80%. Writing a behavioral description takes two minutes. Reviewing AI-generated tests takes another three. You get comprehensive test coverage and better design, and you actually do it consistently because the barrier is low enough.

If you’ve tried TDD before and given up, try it again with AI generating the tests. It changes the entire equation.