GitHub Copilot X Tricks I Wish I Knew Sooner

Alright, grab a coffee (or a pint, depending on the time), and let's chat about GitHub Copilot X. I've been using it pretty heavily for the last few months, and honestly, it's changed how I code. But it's not just about accepting every suggestion it throws at you. To really get the most out of it, you need to understand its quirks and learn some advanced techniques. This tripped me up at first, but after some experimentation, I realised how powerful it could be. ## Why Bother with Advanced Techniques? Look, here's the thing: Copilot's great out of the box. It saves time on boilerplate and simple tasks. But if you're working on complex projects, relying on the basic features will only get you so far. You'll end up spending more time correcting its mistakes than actually coding. These advanced techniques let you steer Copilot in the right direction, ensuring it generates code that's not just syntactically correct but also aligned with your project's architecture and coding style. Plus, let's be real, AI-assisted coding is the future. By 2025, it'll be even more integrated into our workflows. Learning these skills now will give you a serious edge. ## My First Mistake: Trusting It Too Much I jumped in headfirst, accepting almost every suggestion. Big mistake. I quickly realised that Copilot wasn't always producing the *best* code, just the *easiest* code. It was often repetitive and didn't consider the bigger picture. I ended up with a codebase that was functional but messy and hard to maintain. That's when I knew I needed to change my approach. ## Technique 1: Prompt Engineering – Guiding the AI This is probably the most important thing I've learned. Copilot responds to prompts, just like any other AI model. The better your prompts, the better the output. It's all about giving it enough context to understand what you want. ### Be Specific Don't just write a vague comment like // Create a function to fetch data. Instead, be precise: // Create a function called fetchData that fetches data from the /api/users endpoint and returns a JSON object See the difference? The more information you provide, the better Copilot can understand your intentions. ### Use Docstrings Docstrings are your friend. They not only document your code but also provide valuable context for Copilot. I make it a habit to write detailed docstrings *before* writing the actual code. Copilot can then use this information to generate the function body.

python def calculate_average(numbers: list[float]) -> float: """Calculates the average of a list of numbers. Args: numbers: A list of floating-point numbers. Returns: The average of the numbers in the list. Returns 0 if the list is empty. """ # Copilot will generate the function body based on the docstring pass

### Example: Refactoring with Prompts I ran into this last month when I had a huge function that needed refactoring. Instead of trying to rewrite it from scratch, I used Copilot to break it down into smaller, more manageable functions. I added comments like: // Extract the data validation logic into a separate function called validateData Copilot then generated the new validateData function, making the original function much cleaner. ## Technique 2: Leveraging Context – Feeding It Examples Copilot learns from your existing code. If you have a consistent coding style and architecture, it will pick up on it and generate code that fits in seamlessly. The key is to provide it with enough context. ### Open Relevant Files Make sure the files that contain related code are open in your editor. This gives Copilot a broader understanding of your project's structure and dependencies. ### Use Existing Patterns If you have a specific pattern for handling errors or logging, make sure Copilot is aware of it. You can do this by showing it examples of how you've implemented these patterns in other parts of your code. For example, if you have a custom error handling function, use it in a few places, and Copilot will start suggesting it automatically. ### Example: Consistent Error Handling Let's say you have a function called handleError that logs errors and displays a user-friendly message. To make sure Copilot uses this function consistently, you can do something like this:

javascript async function fetchData(url) { try { const response = await fetch(url); const data = await response.json(); return data; } catch (error) { handleError(error, 'Failed to fetch data'); return null; } } // Now, when you write similar functions, Copilot will suggest using handleError

## Technique 3: Fine-Tuning Suggestions – The Art of Rejection Not every suggestion is a good one. It's crucial to learn how to reject suggestions and guide Copilot towards the right solution. This is where the "X" in Copilot X really shines, with the ability to explain and refine suggestions.