My Real Experience with GitHub Copilot (The Good, The Annoying, and The Weird)
So here’s the thing. I was skeptical about GitHub Copilot when it first dropped. Like, genuinely skeptical. I’d been coding for about eight years at that point, and the idea of an AI writing code for me felt like one of those Silicon Valley promises that sounds amazing in a demo but falls apart the second you try to use it on a real project. I was wrong. Mostly. Let me explain.
How It Actually Works Under the Hood
Copilot is built on top of OpenAI’s large language models. It looks at the code you’ve already written — your function names, your comments, your imports, the patterns in your file — and uses that context to predict what you’re going to type next. Sometimes it suggests a single line. Sometimes it writes an entire function. And sometimes it suggests something completely bonkers that makes you wonder if the AI was trained on someone’s homework assignment from 2003.
It runs as an extension in Visual Studio Code, which is where most developers I know already live. You type, and suggestions appear inline as grayed-out text. Tab to accept, Esc to dismiss. Simple.
What It’s Good At
- Code Completion: This is the bread and butter. Copilot is genuinely good at completing function bodies, especially for common patterns. If I’m writing a REST API endpoint or a database query, it often nails the suggestion on the first try.
- Documentation: I’ll be honest, I’m terrible at writing docstrings. Copilot handles this surprisingly well. It generates comments and documentation that are — wait for it — actually accurate most of the time.
- Learning New Languages: I picked up Go last year, and Copilot was like having a patient tutor looking over my shoulder. It would suggest idiomatic Go patterns that I wouldn’t have known to write myself.
- Multi-language Support: Works with JavaScript, Python, TypeScript, Ruby, Go, Rust, and a bunch more. The quality varies by language, but the major ones are well covered.
Where It Genuinely Helps
Probably should have led with this, but the biggest win is boilerplate. All that repetitive code you write a hundred times a week — form validation, API calls, data transformation, test setup — Copilot eats that stuff for breakfast. I’d estimate it saves me 30 to 45 minutes on a good day. Not because the code is hard, but because typing it out is tedious, and Copilot lets me skip straight to the interesting problems.
For teams, there’s a consistency benefit too. Copilot tends to suggest patterns that align with what’s already in your codebase, so new team members end up writing code that fits the project’s style. Makes code reviews faster because there are fewer “why did you do it this way?” conversations.
And for learning? Seriously underrated. When I was exploring unfamiliar APIs, Copilot would suggest usage patterns that saved me from digging through documentation for 20 minutes. Not a replacement for understanding what you’re doing, but a solid accelerator.
Where It Falls Flat
Okay, honesty time. Copilot is wrong more often than the marketing would have you believe. It suggests code that looks right but has subtle bugs. Off-by-one errors. Wrong variable names. Logic that almost works but misses an edge case. If you’re not paying attention and just tab-accepting everything, you’ll introduce bugs. I learned this the hard way on a payment processing feature. Caught it in testing, thankfully, but still.
There’s also the over-reliance problem. I’ve noticed junior developers on my team sometimes accept Copilot’s suggestions without really understanding them. That’s dangerous. The tool should augment your thinking, not replace it. If you can’t explain why a piece of code works, you shouldn’t be shipping it — regardless of whether a human or an AI wrote it.
And then there’s the plagiarism question. Copilot was trained on public code repositories, and sometimes its suggestions look a lot like existing open-source code. This has raised real licensing concerns. It’s something to be aware of, especially if you’re working on proprietary software.
Real-world Use Cases
That’s what makes Copilot endearing to developers across the spectrum — it adapts to wherever you are. In schools and bootcamps, it’s being used as a teaching aid. Students can see how experienced code patterns look without waiting for a TA. In professional settings, it speeds up feature development. Open-source projects are seeing more contributions because the barrier to writing that first pull request is a bit lower. Enterprise teams use it to maintain large codebases more efficiently.
Getting Started
Setup is dead simple. Install Visual Studio Code if you haven’t already. Search for “GitHub Copilot” in the extensions marketplace. Install it. Sign in with your GitHub account. You’ll need a subscription — there’s a free tier for students and open-source maintainers, and a paid plan for everyone else.
Once it’s running, just start coding. Suggestions appear automatically. Tab accepts them, Esc rejects them. You can also cycle through alternative suggestions with keyboard shortcuts. Takes about ten minutes to get comfortable with the flow.
Tips from Someone Who’s Used It Daily
- Know What You’re Building First: Copilot works best when you have a clear idea of what you need. Vague context produces vague suggestions.
- Always Review the Output: Treat every suggestion like code from a junior developer. Read it. Test it. Don’t blindly trust it.
- Write Good Comments: Copilot uses your comments as context. A well-written comment above a function often produces better suggestions than no comment at all.
- Keep Things Updated: The extension improves regularly. Stay current for the best experience.
The Community Around It
There’s a pretty active community of Copilot users. People share tips, report bugs, and discuss best practices on GitHub discussions and various dev forums. GitHub and OpenAI are fairly responsive to feedback too — I’ve seen user-reported issues get addressed in subsequent updates. If you run into something weird, chances are someone else has too, and there’s probably a thread about it.
How It Stacks Up Against Alternatives
Microsoft’s IntelliCode offers similar autocomplete features, but it’s more conservative in its suggestions. Copilot is bolder — it’ll suggest whole functions where IntelliCode might suggest a variable name. Amazon’s CodeWhisperer is another competitor worth looking at, especially if you’re deep in the AWS ecosystem. But for general-purpose AI code assistance, Copilot is still the one to beat in my experience. Though honestly, this space is moving so fast that could change by next quarter.
What’s Coming Next
The trajectory is promising. The AI models keep getting better, which means suggestions get more accurate over time. There’s talk of deeper IDE integration, support for more editors beyond VS Code, and more sophisticated multi-file awareness. The developer community’s feedback is clearly shaping where the product goes, which is encouraging.
Bottom line: GitHub Copilot is a genuinely useful tool that has changed how I write code on a daily basis. It’s not magic, it’s not perfect, and it’s definitely not going to replace developers anytime soon. But if you go in with realistic expectations and good habits around reviewing its output, it’ll make you faster. And in this business, faster matters.
