GitHub Copilot for Code Generation: What Actually Works (And What Doesn’t)

Reader question: “I see everyone talking about AI coding assistants, but I’m confused about what actually works. Should I pay for GitHub Copilot? Does it replace learning to code? And how do I know when to trust what it generates?”

Fair questions. I’ve been using Copilot daily for the past year across Python, JavaScript, and Rust projects. Some days it feels like having a junior developer who never gets tired. Other days it confidently generates garbage that looks right but breaks in subtle ways.

Let me show you what I’ve learned.

The trick isn’t whether to use it – most developers I know are already experimenting with AI code tools. The trick is knowing when to lean on it hard and when to ignore it completely. That line’s blurrier than the marketing suggests.

What GitHub Copilot Actually Does

Copilot sits in your IDE (VS Code, JetBrains, Neovim, whatever) and suggests code as you type. It’s powered by OpenAI models trained on public code repositories. The inline suggestions appear in gray text. You hit Tab to accept, or keep typing to ignore.

Sounds simple. Is simple, really.

But here’s what surprised me during real use: the quality varies wildly depending on context. In a Python file with clear imports and type hints, it nails function completions on the first try. In a legacy JavaScript file with inconsistent naming? It flails around suggesting whatever pattern it saw most often in training data.

I tested this systematically by starting identical projects in different languages. For a basic REST API, the Python suggestions were usable immediately. The Go suggestions needed more hand-holding. The Elixir suggestions were hit-or-miss – sometimes idiomatic, sometimes hilariously wrong. Turns out there’s just less Elixir in the training corpus, so it falls back on patterns from other languages that don’t translate well.

Getting Started: The Setup That Works

You’ll need a GitHub account and a paid subscription ($10/month for individuals, $19/month for businesses). Students and open source maintainers can get it free – check GitHub’s education program.

Installing the Extension

In VS Code: Extensions sidebar → search “GitHub Copilot” → install → sign in with your GitHub account. Takes maybe two minutes.

First time I opened a file after installing, suggestions started appearing immediately. No configuration needed. That was nice – I hate fiddling with settings before I can even try something.

The Settings That Matter

I changed exactly three settings from defaults:

1. Disabled inline suggestions in markdown files. It kept trying to autocomplete my documentation in ways that made no sense.

2. Enabled the Copilot Chat panel (separate feature, rolled out last year). Lets you ask questions without leaving your editor.

3. Set a keyboard shortcut for “show alternative suggestions” – by default you only see one suggestion at a time, but there are usually 3-4 options available. Cycling through them saves a ton of time.

How I Actually Use It (The Workflow That Emerged)

I didn’t plan this workflow. It evolved through dozens of small projects where I noticed what worked and what wasted time.

Step 1: Write the Function Signature First

Don’t just open a blank file and hope Copilot reads your mind. Write the function name, parameters, and return type. Then let it suggest the body.

def calculate_moving_average(prices: list[float], window: int) -> list[float]:
 # Copilot suggestions appear here
 pass

With that context, I got a correct sliding window implementation on the first suggestion. Without type hints? It guessed I wanted a simple average and generated the wrong algorithm entirely.

Step 2: Feed It Context Through Comments

This feels weird at first but becomes natural fast. Before asking Copilot to generate complex logic, write a comment explaining what you need. Specific details matter.

# Parse the CSV, handle missing values by forward-filling,
# convert timestamps to UTC, then group by user_id
def process_user_data(filepath: str) -> pd.DataFrame:

That comment gave it enough context to suggest a pandas pipeline that actually matched my requirements. Just “process user data” as a comment? Got back generic CSV reading with none of the transformations I needed.

Step 3: Accept Suggestions in Small Chunks

Here’s my biggest early mistake: I’d let Copilot generate 50+ lines of code in one go, then spend 20 minutes debugging weird edge cases in the middle.

Better approach: accept one logical piece at a time. For that data processing function, I accepted the CSV reading first. Ran it, verified it worked. Then accepted the timestamp conversion. Tested that. Then the grouping. Step by step.

Takes longer upfront. Saves way more time than fixing a broken 100-line function where you’re not sure which part went wrong.

Pro tip from painful experience: If Copilot suggests error handling you didn’t ask for, read it carefully before accepting. I’ve seen it add try-catch blocks that silently swallow exceptions instead of logging them. The code runs, but you lose visibility into failures. Always check what it’s doing with errors.

Step 4: Use Chat for Explanations, Not Generation

The Copilot Chat feature surprised me. I expected it to be a gimmick. Turns out it’s genuinely useful – but not for the obvious use case.

Asking it to generate code from scratch in chat? Meh. The inline suggestions already do that better because they have your actual file context.

Where chat shines: explaining unfamiliar code. I inherited a Flask app with some gnarly SQLAlchemy queries I didn’t immediately understand. Selected the query, asked chat “what does this do and why is it structured this way?”, got a breakdown that would’ve taken me 15 minutes of reading docs to piece together myself.

Also good for: “why isn’t this working?” when you’re stuck. It can’t see your runtime state, but it catches obvious mistakes like mismatched types or missing imports faster than I do when I’m frustrated.

Where It Falls Short (And My Workarounds)

Let’s talk about the frustrating parts. Because they’re real and I hit them constantly.

Problem: Outdated Patterns

It suggested requests.get(url, verify=False) in Python – disabling SSL verification. That’s a security hole you should basically never use in production. The training data probably included it because developers do it during local testing, but Copilot has no idea when it’s appropriate.

My fix: I keep a mental checklist of risky patterns. Disabled SSL verification, SQL string concatenation, eval(), shell commands with user input. When those show up in suggestions, I stop and think whether I actually want them. Usually I don’t.

Problem: Hallucinated APIs

I asked it to help with a FastAPI endpoint. It suggested using a method request.get_json(). That method doesn’t exist in FastAPI – that’s Flask syntax. The endpoint broke immediately.

This happens a lot with similar frameworks. It mixes up Django and Flask, React and Vue, pytest and unittest. The suggested code looks right because the patterns are adjacent, but the imports and method names don’t match.

My fix: Always check the first suggestion against your framework’s actual docs. Don’t assume method names are correct just because they’re plausible. Takes 10 seconds, prevents annoying bugs.

Problem: Inconsistent Style Across Files

I let it generate functions in three different files for the same project. One used snake_case, one used camelCase, one used abbreviated variable names. All technically valid, but the inconsistency made the codebase feel messy.

My fix: I now start projects with a STYLE.md file that documents naming conventions, and I reference it in comments when generating code. “Follow naming conventions from STYLE.md” in a comment actually works – it picks up on your documented patterns. Not perfectly, but noticeably better than letting it freestyle.

Problem: Test Coverage Gaps

When I ask it to write tests, it focuses on happy paths. Edge cases get ignored. For a function that parsed dates, it generated tests for valid ISO 8601 strings but didn’t test malformed input, empty strings, or None values.

Found this out when production threw an exception on user input I hadn’t anticipated. The tests all passed, but they weren’t comprehensive.

My fix: After Copilot generates tests, I explicitly ask it “what edge cases am I missing?” in chat. It’ll list stuff like boundary conditions, error cases, null handling. Then I write those tests myself because I want to verify they actually fail before the fix.

Copilot vs. Other AI Coding Tools

You’ve got options now. How does Copilot compare?

Claude (via API or web interface) is better at explaining complex code and discussing architectural decisions. When I need to understand why something’s designed a certain way or debate tradeoffs, I reach for Claude. But it’s not built into my editor, so there’s friction. Copilot wins on convenience for in-flow generation.

Cursor IDE has Copilot-like features built in, plus some multi-file awareness that feels sharper. Friends who use it swear by the “edit across multiple files” feature. I haven’t switched because I’m deep in my VS Code setup with custom keybindings and extensions. The migration cost isn’t worth it yet for my workflow.

TabNine is cheaper ($12/month) and has a local model option if you’re worried about sending code to external servers. I tested it briefly. Suggestions felt slightly less accurate than Copilot, but the gap was smaller than I expected. If you’re on a budget or have strict data privacy requirements, worth considering.

Real Talk: Is It Worth $10/Month?

For me? Yeah. I’m writing code 6+ hours most days. Copilot probably saves me an hour of typing and documentation-reading per week. That’s a good return.

For someone just learning to code? I’m less sure. There’s value in the rote practice of typing out syntax patterns until they’re muscle memory. Copilot shortcuts that. You’ll learn faster if you understand what the code does before you let a tool generate it.

That said, I’ve watched beginner developers use Copilot as a learning tool – accepting a suggestion, then stepping through it line by line to understand how it works. That approach seems productive. It’s when people blindly trust suggestions without reading them that problems start.

One thing I’m certain about: this category of tool isn’t going away. Whether it’s Copilot specifically or something else, AI-assisted coding is becoming standard practice. Learning to use it effectively matters more than debating whether to use it at all.

Frequently Asked Questions

Does using Copilot mean I don’t need to learn to code?

God, no. Copilot generates code based on patterns it saw in training data. You need to understand what that code does to know if it’s correct for your situation. It’s like having a thesaurus – useful if you know what word you’re looking for, useless if you don’t understand the underlying language.

Can I use Copilot for free?

If you’re a student (verified through GitHub Education) or a maintainer of popular open source projects, yes. Otherwise it’s $10/month for individual use. There’s a 30-day trial if you want to test it first.

Does Copilot steal my code or leak it to other users?

Your code gets sent to OpenAI’s servers for processing, but according to GitHub’s privacy terms, they don’t use it for training and it’s not shown to other users. If you’re working on proprietary code under NDA, check with your legal team. Some companies ban tools that send code externally. Others are fine with it.

What languages work best with Copilot?

Python, JavaScript, TypeScript, Java, and Go have the most training data, so suggestions tend to be highest quality there. I’ve had decent luck with Rust and C++. Less common languages like Haskell, Clojure, or Erlang are hit-or-miss because there’s less public code to learn from.

What to Try Next

Pick something small and annoying you’ve been putting off. A script to rename files in a specific pattern. A function to parse some weird API response format. Whatever.

Open your editor with Copilot enabled. Write the function signature and a comment describing what you need. See what it suggests. Accept it, run it, and notice what breaks. Fix the breakage. That cycle – suggest, run, break, fix – teaches you way more about what this tool can and can’t do than any tutorial.

You’ll figure out your own workflow. Mine’s probably different from yours because we code differently. That’s fine. The goal isn’t to use Copilot exactly how I do. The goal’s to find the spots in your process where it genuinely helps and ignore the marketing hype about everything else.