GitHub Copilot: First Impressions After Getting a License Link to heading

After several months of copy-pasting solutions from ChatGPT, I finally got a GitHub Copilot license in February 2024. Having grown accustomed to ChatGPT’s conversational AI assistance, I was curious how Copilot’s inline suggestions would compare. The answer: they’re complementary tools that have transformed my development workflow in different but equally valuable ways.

Coming from ChatGPT Copy-Paste Link to heading

For the past several months, my AI-assisted development workflow involved:

1. Describe problem to ChatGPT in natural language
2. Get a solution and explanation
3. Copy code back to VS Code
4. Adapt and integrate

This worked well but required constant context switching between browser and IDE. Copilot eliminates that friction by bringing AI assistance directly into the coding environment.

First Impressions Link to heading

The initial setup was remarkably simple; just install the VS Code extension and start coding. The first time Copilot suggested a complete function implementation from just a comment, it felt genuinely magical:

// Function to validate email format and check if domain exists
function validateEmail(email) {
  // Copilot suggested this entire implementation:
  const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/
  if (!emailRegex.test(email)) {
    return { valid: false, reason: "Invalid format" }
  }

  const domain = email.split("@")[1]
  // Additional domain validation logic...
  return { valid: true }
}

The suggestions felt eerily accurate, as if Copilot understood not just what I wanted to do, but how I preferred to structure my code.

Where Copilot Excels Link to heading

Boilerplate Generation Link to heading

Copilot shines brightest when generating repetitive code patterns. Writing API routes, database queries, and test cases became significantly faster:

// Just writing this comment and function signature:
// Create CRUD operations for User model

async function createUser(userData) {
  // Copilot completed the entire implementation
  try {
    const user = new User(userData)
    await user.validate()
    await user.save()
    return { success: true, user: user.toJSON() }
  } catch (error) {
    return { success: false, error: error.message }
  }
}

The suggestions often include error handling, validation, and edge cases I might have forgotten to implement manually.

Language Translation Link to heading

When working across different programming languages, Copilot proved remarkably good at translating patterns:

# After writing similar JavaScript functions, Copilot suggested:
def format_currency(amount, currency="AUD"):
    """Format amount as currency string"""
    return f"${amount:,.2f} {currency}"

The suggestions maintained consistent naming conventions and patterns across languages, which is particularly valuable when maintaining polyglot codebases.

Documentation and Comments Link to heading

Copilot has encouraged better documentation habits by making it effortless:

/**
 * Copilot suggested this detailed JSDoc comment:
 * Calculates the compound interest for a given principal, rate, and time period
 * @param principal - The initial amount of money
 * @param rate - The annual interest rate (as a decimal)
 * @param time - The number of years
 * @param compoundFrequency - How many times per year interest is compounded
 * @returns The final amount after compound interest
 */
function calculateCompoundInterest(principal: number, rate: number, time: number, compoundFrequency: number = 12): number {
  return principal * Math.pow(1 + rate / compoundFrequency, compoundFrequency * time)
}

Limitations and Gotchas Link to heading

Context Understanding Link to heading

While Copilot is impressive, it doesn’t understand broader project context. It might suggest patterns that work in isolation but conflict with existing architecture:

// Copilot suggested using a different error handling pattern
// than the rest of my project
try {
  await someOperation()
} catch (err) {
  console.error(err) // My project uses structured logging
  throw err // My project has custom error classes
}

Security and Privacy Concerns Link to heading

Early in my usage, I noticed Copilot occasionally suggested code that included what looked like API keys or sensitive data from its training set:

// Concerning suggestion that appeared to include real credentials
const config = {
  apiKey: "sk-1234567890abcdef...", // This looked like a real key
  endpoint: "https://api.example.com",
}

This highlighted the importance of code review and never committing suggested code without understanding what it does.

Over-reliance Risk Link to heading

The most subtle danger has been the temptation to accept suggestions without fully understanding them. I caught myself doing this with complex algorithms:

// Copilot suggested a sorting algorithm I didn't immediately recognise
function quickSort(arr) {
  if (arr.length <= 1) return arr
  const pivot = arr[Math.floor(arr.length / 2)]
  // ... complex implementation I needed to study
}

I’ve made it a rule to only accept suggestions I can explain and maintain.

Impact on Development Workflow Link to heading

Faster Prototyping Link to heading

Initial feature development became significantly faster. Copilot’s suggestions often provided a solid foundation that I could then refine:

// Building a REST API endpoint from scratch
app.post("/users/:id/preferences", async (req, res) => {
  // Copilot provided structure, I refined the specifics
  try {
    const userId = req.params.id
    const preferences = req.body

    const user = await User.findById(userId)
    if (!user) {
      return res.status(404).json({ error: "User not found" })
    }

    user.preferences = { ...user.preferences, ...preferences }
    await user.save()

    res.json({ success: true, preferences: user.preferences })
  } catch (error) {
    res.status(500).json({ error: error.message })
  }
})

Learning Accelerator Link to heading

Copilot exposed me to patterns and approaches I might not have discovered otherwise. It became a learning tool as much as a productivity aid:

// I was learning Rust, and Copilot suggested idiomatic patterns:
use std::collections::HashMap;

fn count_words(text: &str) -> HashMap<String, usize> {
    text.split_whitespace()
        .map(|word| word.to_lowercase())
        .fold(HashMap::new(), |mut acc, word| {
            *acc.entry(word).or_insert(0) += 1;
            acc
        })
}

Testing and Quality Assurance Link to heading

Test Generation Link to heading

Copilot proved particularly useful for generating test cases:

describe("validateEmail", () => {
  // Copilot suggested comprehensive test cases
  test("should return valid for correct email format", () => {
    expect(validateEmail("test@example.com")).toEqual({
      valid: true,
    })
  })

  test("should return invalid for missing @ symbol", () => {
    expect(validateEmail("testexample.com")).toEqual({
      valid: false,
      reason: "Invalid format",
    })
  })

  // Generated edge cases I might have missed
  test("should return invalid for multiple @ symbols", () => {
    expect(validateEmail("test@@example.com")).toEqual({
      valid: false,
      reason: "Invalid format",
    })
  })
})

However, I learned to be cautious about test quality; Copilot sometimes suggested tests that passed but didn’t actually validate the intended behaviour.

Performance Considerations Link to heading

Suggestion Quality vs Speed Link to heading

I noticed that pausing briefly before accepting suggestions led to better results. Copilot seemed to provide more thoughtful suggestions when given a moment to “think”:

// Quick acceptance often led to generic solutions
function processData(data) {
  return data.map((item) => item.value)
}

// Pausing led to more contextually appropriate suggestions
function processUserAnalytics(rawUserData) {
  return rawUserData
    .filter((user) => user.isActive)
    .map((user) => ({
      id: user.id,
      engagementScore: calculateEngagement(user.activities),
      lastActive: user.lastLoginDate,
    }))
    .sort((a, b) => b.engagementScore - a.engagementScore)
}

Team Collaboration Impact Link to heading

Code Style Consistency Link to heading

Interestingly, Copilot helped maintain code style consistency across the team, as it learned from existing codebase patterns:

// Copilot adapted to our team's error handling pattern
const result = await apiCall().catch((error) => {
  logger.error("API call failed:", {
    error: error.message,
    stack: error.stack,
  })
  throw new ServiceError("External API unavailable", error)
})

Knowledge Sharing Link to heading

Junior developers on the team found Copilot particularly valuable for learning established patterns and best practices without needing constant mentorship.

Ethical and Legal Considerations Link to heading

The early adoption period raised important questions about code ownership and licensing. Copilot’s training on public repositories meant suggestions might include copyrighted code, requiring careful consideration of intellectual property implications.

I adopted a policy of treating Copilot suggestions as inspiration rather than final solutions, always reviewing and modifying suggestions to fit project requirements and coding standards.

Future Implications Link to heading

Changing Skill Requirements Link to heading

Copilot has shifted some of the value from remembering syntax to understanding concepts and architecture. The ability to read, evaluate, and modify AI-generated code is becoming as important as writing code from scratch.

Development Process Evolution Link to heading

The traditional write-test-debug cycle has evolved to include an “evaluate AI suggestion” step. This requires developing new skills around quickly assessing code quality and correctness.

Key Takeaways Link to heading

After extensive use during the early adoption period, several principles emerged:

1. Use as a Starting Point: Treat suggestions as first drafts, not final solutions
2. Maintain Understanding: Only accept code you can explain and maintain
3. Review Everything: Never commit AI-generated code without careful review
4. Preserve Context: Ensure suggestions fit your project’s patterns and architecture
5. Stay Curious: Use suggestions as learning opportunities to discover new approaches

Looking Forward Link to heading

GitHub Copilot represents the beginning of AI-assisted development rather than its culmination. The technology will undoubtedly improve, and the development community will continue to establish best practices for human-AI collaboration in coding.

The key is viewing AI tools as powerful assistants rather than replacements for human judgment and creativity. The most effective approach combines AI efficiency with human insight and oversight.

As we move toward general availability and more sophisticated AI coding tools, the developers who thrive will be those who learn to collaborate effectively with AI while maintaining their problem-solving skills and architectural thinking.

Have you experimented with GitHub Copilot or other AI coding tools? What has your experience been like, and how do you see AI changing the way we write code?