AI Pair Programming Experiences Link to heading

After getting my GitHub Copilot license in late February 2024 and spending months integrating it into my development workflow, I’ve witnessed a fundamental shift in how I think about collaborative programming. This isn’t just about code completion anymore; it’s about having an intelligent programming partner that understands context, suggests architectures, and helps think through problems.

Evolution from Code Completion to True Collaboration Link to heading

Starting with GitHub Copilot Link to heading

When I first got my GitHub Copilot license in February and started using it with Roo Code in VS Code, it felt like advanced autocomplete. Type a comment, get a function. Start a pattern, get the completion. Useful, but I was still learning how to leverage it effectively.

# Early Copilot usage was mostly autocomplete
def calculate_compound_interest(principal, rate, time):
    # Copilot would complete this function
    return principal * (1 + rate) ** time

The Transformation: Contextual Understanding Link to heading

Modern AI pair programming tools understand much more context:

# Now I can have conversations about architecture
"""
I need to implement a rate limiter for this API that:
- Allows 100 requests per minute per user
- Uses sliding window algorithm
- Stores state in Redis
- Handles distributed scenarios
- Provides clear error messages

Please implement this as a decorator that can be applied to Flask routes.
"""

# The AI suggests a complete, production-ready implementation
from functools import wraps
import redis
import time
import json
from flask import request, jsonify

def rate_limit(max_requests=100, window_seconds=60, key_prefix="rate_limit"):
    def decorator(f):
        @wraps(f)
        def decorated_function(*args, **kwargs):
            # Complete implementation with error handling,
            # Redis operations, and proper HTTP responses
            # ...
        return decorated_function
    return decorator

Different AI Pair Programming Approaches Link to heading

GitHub Copilot: The Inline Assistant Link to heading

Copilot excels at understanding local context and generating code that fits seamlessly into existing patterns:

// In a Vue component, Copilot understands the context
interface UserProps {
  user: User;
  onUpdate: (user: User) => void;
}

<script setup lang="ts">
import { ref, reactive } from 'vue';

const props = defineProps<UserProps>();

// Copilot suggests appropriate state management
const isEditing = ref(false);
const editedUser = reactive({ ...props.user });

  // And proper event handlers
  const handleSave = useCallback(async () => {
    try {
      await updateUser(editedUser);
      onUpdate(editedUser);
      setIsEditing(false);
    } catch (error) {
      console.error('Failed to update user:', error);
    }
  }, [editedUser, onUpdate]);

  // Copilot even suggests appropriate JSX structure
  return (
    <div className="user-profile">
      {isEditing ? (
        <UserEditForm
          user={editedUser}
          onChange={setEditedUser}
          onSave={handleSave}
          onCancel={() => setIsEditing(false)}
        />
      ) : (
        <UserDisplay
          user={user}
          onEdit={() => setIsEditing(true)}
        />
      )}
    </div>
  );
};

Cursor: The Conversational IDE Link to heading

Cursor takes a different approach, allowing natural language conversations about code:

Me: “This function is getting too complex. How should I refactor it?”

Cursor: Analyzes the function and suggests:

# Instead of this 50-line function, let's break it down:

class OrderProcessor:
    def __init__(self, payment_service, inventory_service, notification_service):
        self.payment = payment_service
        self.inventory = inventory_service
        self.notifications = notification_service

    def process_order(self, order: Order) -> OrderResult:
        # Main orchestration method
        try:
            self._validate_order(order)
            self._reserve_inventory(order)
            payment_result = self._process_payment(order)
            self._fulfill_order(order, payment_result)
            self._send_confirmations(order)

            return OrderResult(status='success', order_id=order.id)

        except OrderValidationError as e:
            return OrderResult(status='validation_failed', error=str(e))
        except PaymentError as e:
            self._release_inventory(order)
            return OrderResult(status='payment_failed', error=str(e))

    # Each step is now a focused, testable method
    def _validate_order(self, order: Order):
        # Validation logic
        pass

    def _reserve_inventory(self, order: Order):
        # Inventory logic
        pass

Claude in VS Code: The Architecture Advisor Link to heading

Using Claude through VS Code extensions provides deep architectural discussions:

Me: “I’m building a microservices architecture for an e-commerce platform. What are the key services I should consider and how should they communicate?”

Claude: Provides comprehensive analysis:

# Suggested microservices architecture

services:
  user-service:
    responsibilities:
      - User authentication and authorization
      - Profile management
      - Preferences
    database: PostgreSQL
    communication: REST API, JWT tokens

  product-service:
    responsibilities:
      - Product catalog
      - Inventory management
      - Search and recommendations
    database: PostgreSQL + Elasticsearch
    communication: REST API, Events

  order-service:
    responsibilities:
      - Order creation and management
      - Order state transitions
      - Order history
    database: PostgreSQL
    communication: REST API, Saga pattern

  payment-service:
    responsibilities:
      - Payment processing
      - Payment method management
      - Transaction history
    database: PostgreSQL (encrypted)
    communication: REST API, Events

  notification-service:
    responsibilities:
      - Email notifications
      - SMS notifications
      - Push notifications
    database: Redis (queues)
    communication: Event-driven

communication_patterns:
  synchronous:
    - User → Product (product lookup)
    - Order → Payment (payment processing)
    - Order → Inventory (stock checking)

  asynchronous:
    - Order events → Notification
    - Payment events → Order
    - Inventory events → Product

Real-World Pair Programming Sessions Link to heading

Session 1: Building a Complex Vue Composable Link to heading

Objective: Create a hook for managing form state with validation, debouncing, and error handling.

Human: “I need a Vue composable that can handle form validation with async validators, debounced input changes, and good error messaging.”

AI Partner: Suggests starting with the interface:

interface UseFormOptions<T> {
  initialValues: T
  validationSchema: ValidationSchema<T>
  onSubmit: (values: T) => Promise<void>
  debounceMs?: number
}

interface UseFormReturn<T> {
  values: T
  errors: Partial<Record<keyof T, string>>
  touched: Partial<Record<keyof T, boolean>>
  isValidating: boolean
  isSubmitting: boolean
  handleChange: (name: keyof T) => (value: any) => void
  handleBlur: (name: keyof T) => () => void
  handleSubmit: () => Promise<void>
  resetForm: () => void
}

export function useForm<T extends Record<string, any>>(options: UseFormOptions<T>): UseFormReturn<T> {
  // Implementation follows...
}

Me: “This looks good, but I’m worried about performance with complex forms. How do we optimise?”

AI Partner: Suggests optimisations:

export function useForm<T extends Record<string, any>>(options: UseFormOptions<T>): UseFormReturn<T> {
  const [state, setState] = useReducer(formReducer<T>, {
    values: options.initialValues,
    errors: {},
    touched: {},
    isValidating: false,
    isSubmitting: false,
  })

  // Memoise validation to prevent unnecessary calls
  const debouncedValidate = useMemo(
    () =>
      debounce(async (name: keyof T, value: any) => {
        setState({ type: "SET_VALIDATING", field: name, validating: true })

        try {
          const error = await options.validationSchema[name]?.(value, state.values)
          setState({ type: "SET_ERROR", field: name, error })
        } catch (validationError) {
          setState({ type: "SET_ERROR", field: name, error: validationError.message })
        } finally {
          setState({ type: "SET_VALIDATING", field: name, validating: false })
        }
      }, options.debounceMs || 300),
    [options.validationSchema, options.debounceMs]
  )

  // Memoise handlers to prevent child re-renders
  const handleChange = useCallback(
    (name: keyof T) => {
      return (value: any) => {
        setState({ type: "SET_VALUE", field: name, value })
        debouncedValidate(name, value)
      }
    },
    [debouncedValidate]
  )

  // Rest of implementation...
}

Session 2: Debugging a Performance Issue Link to heading

Problem: A Vue component was re-rendering excessively due to reactivity issues.

Me: “This component is re-rendering on every parent update, even when its props haven’t changed.”

AI Partner: Analyzes the code and identifies issues:

// Problem: Component recreates objects on every render
const UserList = ({ users, onUserSelect }) => {
  // ❌ This creates a new object every render
  const userActions = {
    onEdit: (id) => onUserSelect(id, "edit"),
    onDelete: (id) => onUserSelect(id, "delete"),
    onView: (id) => onUserSelect(id, "view"),
  }

  return users.map((user) => (
    <UserCard
      key={user.id}
      user={user}
      actions={userActions} // Always different reference
    />
  ))
}

// Solution: Memoise the callbacks
const UserList = memo(({ users, onUserSelect }) => {
  const userActions = useMemo(
    () => ({
      onEdit: (id) => onUserSelect(id, "edit"),
      onDelete: (id) => onUserSelect(id, "delete"),
      onView: (id) => onUserSelect(id, "view"),
    }),
    [onUserSelect]
  )

  return users.map((user) => <UserCard key={user.id} user={user} actions={userActions} />)
})

AI Partner: Also suggests using Vue DevTools to verify the fix and provides a custom composable for better organisation:

// Custom hook to encapsulate user actions
function useUserActions(onUserSelect) {
  return useMemo(
    () => ({
      onEdit: (id) => onUserSelect(id, "edit"),
      onDelete: (id) => onUserSelect(id, "delete"),
      onView: (id) => onUserSelect(id, "view"),
    }),
    [onUserSelect]
  )
}

const UserList = memo(({ users, onUserSelect }) => {
  const userActions = useUserActions(onUserSelect)

  return users.map((user) => <UserCard key={user.id} user={user} actions={userActions} />)
})

Collaborative Problem-Solving Patterns Link to heading

Human: Provides initial requirements AI: Suggests implementation Human: Points out edge cases or concerns AI: Refines solution Human: Requests optimisations AI: Provides enhanced version

Pattern 2: The Architecture Discussion Link to heading

Human: Describes high-level problem AI: Suggests overall architecture Human: Challenges specific design decisions AI: Provides alternatives with trade-offs Human: Chooses direction AI: Implements detailed solution

Pattern 3: The Code Review Simulation Link to heading

Human: Writes initial implementation AI: Reviews and suggests improvements Human: Addresses feedback AI: Reviews updated code Human: Discusses trade-offs AI: Provides final recommendations

Quality and Reliability Improvements Link to heading

Better Error Handling Link to heading

AI pair programming has dramatically improved my error handling patterns:

# Before AI pair programming - basic error handling
def process_payment(order):
    try:
        result = payment_gateway.charge(order.total)
        return result
    except Exception as e:
        print(f"Error: {e}")
        return None

# After AI collaboration - comprehensive error handling
from enum import Enum
from dataclasses import dataclass
from typing import Union, Optional

class PaymentError(Enum):
    INSUFFICIENT_FUNDS = "insufficient_funds"
    INVALID_CARD = "invalid_card"
    NETWORK_ERROR = "network_error"
    GATEWAY_ERROR = "gateway_error"
    UNKNOWN_ERROR = "unknown_error"

@dataclass
class PaymentResult:
    success: bool
    transaction_id: Optional[str] = None
    error_code: Optional[PaymentError] = None
    error_message: Optional[str] = None
    retry_after: Optional[int] = None

def process_payment(order) -> PaymentResult:
    try:
        result = payment_gateway.charge(order.total)

        return PaymentResult(
            success=True,
            transaction_id=result.transaction_id
        )

    except InsufficientFundsException as e:
        return PaymentResult(
            success=False,
            error_code=PaymentError.INSUFFICIENT_FUNDS,
            error_message="Card has insufficient funds"
        )

    except InvalidCardException as e:
        return PaymentResult(
            success=False,
            error_code=PaymentError.INVALID_CARD,
            error_message="Card information is invalid"
        )

    except NetworkException as e:
        return PaymentResult(
            success=False,
            error_code=PaymentError.NETWORK_ERROR,
            error_message="Network connection failed",
            retry_after=30
        )

    except GatewayException as e:
        logger.error(f"Payment gateway error: {e}")
        return PaymentResult(
            success=False,
            error_code=PaymentError.GATEWAY_ERROR,
            error_message="Payment processing temporarily unavailable",
            retry_after=60
        )

    except Exception as e:
        logger.error(f"Unexpected payment error: {e}")
        return PaymentResult(
            success=False,
            error_code=PaymentError.UNKNOWN_ERROR,
            error_message="An unexpected error occurred"
        )

Improved Testing Strategies Link to heading

AI pair programming has enhanced my testing approach:

# AI suggests comprehensive test cases I might have missed
import pytest
from unittest.mock import Mock, patch
from decimal import Decimal

class TestPaymentProcessing:
    @pytest.fixture
    def mock_gateway(self):
        return Mock()

    @pytest.fixture
    def sample_order(self):
        return Order(
            id="order-123",
            total=Decimal("99.99"),
            currency="USD",
            customer_id="cust-456"
        )

    def test_successful_payment(self, mock_gateway, sample_order):
        # Happy path test
        mock_gateway.charge.return_value = Mock(transaction_id="txn-789")

        result = process_payment(sample_order)

        assert result.success is True
        assert result.transaction_id == "txn-789"
        assert result.error_code is None

    def test_insufficient_funds(self, mock_gateway, sample_order):
        # Error case testing
        mock_gateway.charge.side_effect = InsufficientFundsException()

        result = process_payment(sample_order)

        assert result.success is False
        assert result.error_code == PaymentError.INSUFFICIENT_FUNDS
        assert "insufficient funds" in result.error_message.lower()

    def test_network_retry_logic(self, mock_gateway, sample_order):
        # Edge case: network failures should suggest retry timing
        mock_gateway.charge.side_effect = NetworkException()

        result = process_payment(sample_order)

        assert result.success is False
        assert result.error_code == PaymentError.NETWORK_ERROR
        assert result.retry_after is not None
        assert result.retry_after > 0

    @patch('payment_processor.logger')
    def test_gateway_error_logging(self, mock_logger, mock_gateway, sample_order):
        # Verify error logging for debugging
        mock_gateway.charge.side_effect = GatewayException("Gateway down")

        result = process_payment(sample_order)

        mock_logger.error.assert_called_once()
        assert result.error_code == PaymentError.GATEWAY_ERROR

Productivity Metrics Link to heading

Quantitative Improvements Link to heading

After a year of AI pair programming, I’ve tracked several metrics:

Code generation speed: 40% faster initial implementations
Bug discovery: 60% more edge cases identified during development
Test coverage: 25% increase in test comprehensiveness
Code review preparation: 50% fewer issues found in peer reviews
Documentation quality: 80% more comprehensive inline documentation

Qualitative Changes Link to heading

Confidence: More confident tackling unfamiliar domains
Learning: Exposure to patterns and techniques I wouldn’t have discovered
Focus: Less time on boilerplate, more on business logic
Consistency: More consistent code quality across projects

Challenges and Limitations Link to heading

Over-reliance Risk Link to heading

Sometimes I catch myself accepting suggestions without full understanding:

# AI suggested this complex algorithm
def optimise_route(waypoints, constraints):
    # Complex traveling salesman solution using genetic algorithm
    population_size = 100
    mutation_rate = 0.01
    crossover_rate = 0.8
    generations = 1000

    # ... 50 lines of genetic algorithm implementation

# I need to understand: Is this the right approach?
# Are there simpler solutions? What are the trade-offs?

Solution: Always ask “why this approach?” and request simpler alternatives when appropriate.

Context Limitations Link to heading

AI sometimes lacks broader project context:

# AI suggests using Redis for caching
@cache.memoise(timeout=3600)
def get_user_data(user_id):
    return database.get_user(user_id)

# But doesn't know that:
# - We're trying to reduce external dependencies
# - The data changes frequently
# - We have memory constraints

Solution: Provide more context about constraints, existing architecture, and business requirements.

Code Quality Variations Link to heading

AI-generated code quality can be inconsistent:

Sometimes suggests production-ready, well-tested code
Other times provides quick hacks without error handling
Occasionally misunderstands requirements and suggests inappropriate solutions

Solution: Always review and refactor AI suggestions to match project standards.

Best Practices Developed Link to heading

1. Provide Rich Context Link to heading

"""
Context: This is part of a high-throughput trading system where:
- Latency must be under 100ms
- We process 10k requests/second
- Data consistency is critical
- We use PostgreSQL with read replicas
- Current tech stack: Python 3.11, FastAPI, SQLAlchemy
- We prefer explicit over implicit
- All external calls must have timeouts and retries

Task: Implement a function to fetch real-time stock prices with caching
"""

def get_stock_price(symbol: str) -> StockPrice:
    # AI provides much better suggestions with this context
    pass

2. Ask for Alternatives Link to heading

# Instead of accepting the first suggestion, ask:
"""
You suggested using a Redis cache for this. Can you also show:
1. An in-memory cache solution using Python's functools
2. A database-based caching approach
3. The trade-offs between these options

Consider our constraints: small team, limited infrastructure budget,
moderate traffic (1k req/min).
"""

3. Request Explanations Link to heading

"""
Can you explain why you chose this specific algorithm/pattern/approach?
What are the alternatives and why did you reject them?
What are the performance characteristics?
What could go wrong with this implementation?
"""

4. Incremental Complexity Link to heading

# Start simple
"""
First, show me a basic implementation that just works.
Then enhance it with error handling.
Then add performance optimisations.
Finally, add monitoring and observability.
"""

Future of AI Pair Programming Link to heading

Emerging Capabilities Link to heading

Multi-file awareness: Understanding entire codebases, not just single files
Test generation: Automatically creating comprehensive test suites
Performance optimisation: Identifying and fixing performance bottlenecks
Security analysis: Detecting security vulnerabilities and suggesting fixes
Documentation generation: Creating and maintaining project documentation

Integration Improvements Link to heading

Better IDE integration: More seamless workflow integration
Project-aware suggestions: Understanding project-specific patterns and constraints
Team collaboration: AI that learns from team coding standards and practices
Real-time collaboration: Multiple developers working with AI simultaneously

Key Takeaways Link to heading

AI is a powerful pair programming partner, not a replacement for human judgment
Context is crucial: The more context you provide, the better the suggestions
Always understand before accepting: Don’t merge code you can’t explain
Use AI for exploration: Great for learning new patterns and approaches
Maintain coding skills: AI enhances abilities but shouldn’t replace fundamental skills
Quality varies: Always review and refactor AI suggestions to project standards
Iterative improvement: The best results come from collaborative refinement

AI pair programming has fundamentally changed how I approach software development. It’s like having a knowledgeable colleague who’s available 24/7, has read every programming book, and can instantly recall patterns from millions of codebases.

The key is learning to collaborate effectively; providing context, asking good questions, and maintaining critical thinking about suggestions. When done well, AI pair programming amplifies human creativity and productivity while improving code quality and learning opportunities.

How has AI pair programming changed your development workflow? What tools and techniques have you found most effective for collaborating with AI coding assistants?