A brutally honest review of GitHub Copilot’s impact on my development workflow

---

After months of hearing fellow developers rave about GitHub Copilot, I finally decided to give it a shot. The promise was tempting: an AI pair programmer that could boost productivity, reduce boilerplate code, and help with complex logic. But does it live up to the hype?

I spent a full week integrating Copilot into my daily workflow, working on everything from React components to Python scripts to database queries. Here’s my unfiltered take on whether this AI assistant actually made me code faster—or just differently.

The Setup: What I Was Working On

To give Copilot a fair test, I worked on a variety of projects during the week:

• Frontend Development: Building a React dashboard with TypeScript
• Backend API: Creating REST endpoints in Node.js/Express
• Data Processing: Writing Python scripts for CSV analysis
• Database Work: Complex SQL queries and migrations
• DevOps Tasks: Docker configurations and CI/CD pipeline adjustments

This variety would help me understand where Copilot shines and where it falls short across different domains.

Day 1: The Honeymoon Phase

My first impression was genuinely impressive. Within minutes of installation, Copilot was suggesting entire function implementations based on my comments and function names.

What blew my mind early on:

• Writing a comment like // Calculate compound interest with monthly compounding and having Copilot generate the entire mathematical function
• Auto-completing repetitive React component patterns I’d written dozens of times
• Generating realistic test data and mock objects instantly

First day productivity boost: ~25%

The time savings came primarily from eliminating boilerplate and repetitive patterns. Instead of typing out familiar structures, I could accept Copilot’s suggestions and move on to the interesting logic.

Days 2-3: Learning to Work Together

As the novelty wore off, I started developing a better workflow with Copilot. The key insight was learning when to trust its suggestions and when to ignore them.

Copilot excelled at:

• Boilerplate elimination: Form validation, error handling, common patterns
• API integrations: Generating HTTP client code for well-known services
• Code completion: Finishing obvious implementations once the pattern was established
• Documentation: Writing comprehensive JSDoc comments
• Test generation: Creating unit test templates and mock data

Where it struggled:

• Business logic: Complex, domain-specific requirements often produced incorrect implementations
• Performance optimization: Suggestions weren’t always the most efficient approach
• Security considerations: Generated code sometimes missed important security practices
• Newer libraries: Less accurate with recently released or niche packages

Mid-week productivity assessment: ~15-20% faster

Days 4-5: The Reality Check

By midweek, I encountered Copilot’s limitations more frequently. The initial magic gave way to a more nuanced understanding of when and how to use it effectively.

Specific challenges I faced:

The Over-Reliance Trap

I caught myself accepting suggestions without fully understanding them, leading to bugs that took longer to debug than if I’d written the code myself.

Context Limitations

Copilot sometimes suggested code that worked in isolation but didn’t fit well with the broader application architecture or existing patterns.

The “Almost Right” Problem

Many suggestions were 80% correct but required careful review and modification. Sometimes it was faster to write from scratch.

Breaking My Flow

Constantly evaluating suggestions could interrupt my thought process, especially when working through complex problems that required deep concentration.

Days 6-7: Finding the Sweet Spot

By the end of the week, I had developed a more strategic approach to using Copilot:

My evolved workflow:

1. Planning phase: Think through the architecture and approach without Copilot
2. Implementation phase: Use Copilot for boilerplate and common patterns
3. Logic phase: Write complex business logic manually, using Copilot for completion
4. Review phase: Carefully audit all AI-generated code

Best practices I developed:

• Always read and understand generated code before accepting
• Use descriptive comments to guide better suggestions
• Leverage Copilot for learning new APIs and libraries
• Disable it during complex problem-solving sessions
• Use it heavily for testing and documentation

The Verdict: Did It Make Me Faster?

Short answer: Yes, but not in the way I expected.

Quantified results from my week:

• Overall coding speed: ~20% increase
• Time spent on boilerplate: ~60% reduction
• Time spent debugging: ~10% increase (due to AI-generated bugs)
• Time spent learning new APIs: ~40% reduction

Where the real value lies:

1. Reduced Cognitive Load

Copilot handles the mundane stuff, letting me focus on architecture and problem-solving. This mental bandwidth is incredibly valuable.

2. Faster Exploration

When working with new libraries or APIs, Copilot accelerated my learning curve significantly. It’s like having documentation that writes code examples.

3. Consistency Improvements

AI-generated code follows consistent patterns, reducing stylistic variations across the codebase.

4. Better Documentation

Copilot encouraged me to write better comments since they directly improved its suggestions.

What I Didn’t Expect

The Learning Accelerator Effect: Copilot exposed me to coding patterns and techniques I might not have discovered otherwise. It’s like pair programming with someone who has read every programming blog ever written.

The Rubber Duck Evolution: Instead of explaining my code to a rubber duck, I found myself writing more descriptive comments for Copilot, which improved my code clarity.

The Testing Revolution: Copilot’s ability to generate comprehensive test cases actually improved my testing discipline.

The Downsides

Skill Atrophy Concerns: There’s a legitimate worry about becoming too dependent on AI assistance for basic coding tasks.

Code Review Overhead: Every suggestion needs careful review, which can slow down experienced developers who typically code quickly.

Context Blindness: Copilot doesn’t understand your specific business logic, coding standards, or architectural decisions.

Cost vs. Benefit: At $10/month, it needs to save you at least 2-3 hours monthly to be cost-effective.

Who Should Use Copilot?

Great for:

• Developers working with multiple languages/frameworks
• Teams with lots of boilerplate code
• Junior developers learning new patterns
• Anyone doing lots of API integration work
• Developers who write comprehensive tests

Maybe skip it if:

• You work primarily on highly specialized, domain-specific code
• Your codebase uses primarily internal libraries and patterns
• You’re concerned about code dependency and want to maintain raw coding skills
• Your work involves primarily complex algorithmic challenges

Final Thoughts: A Tool, Not a Replacement

After a week with Copilot, I’m convinced it’s a valuable addition to my development toolkit—but it’s not a silver bullet. The 20% speed improvement is real, but it comes with trade-offs that require mindful management.

The biggest shift isn’t just coding faster; it’s changing how I approach coding problems. Copilot is best when it handles the boring stuff while I focus on the interesting challenges that require human insight, creativity, and domain expertise.

Will I keep using it? Absolutely. But I’ll use it strategically, as one tool among many in my developer arsenal.

My recommendation: Try it for a month, but approach it as a productivity enhancer, not a thinking replacement. Set boundaries, maintain your core coding skills, and always remember that the most important part of programming isn’t typing—it’s thinking.

---

Have you tried Copilot or other AI coding assistants? What was your experience? I’d love to hear your thoughts in the comments below.