Maximizing Productivity: GitHub Copilot With Custom Instructions in VS Code

AI code assistants really shine when they're integrated into an integrated development environment (IDE). Imagine an IDE as the ultimate workspace where everything a developer could want is right at their fingertips, like syntax highlighting that makes code a breeze to read, error detection that spots issues before they escalate, and version control that tracks every little change. In this dynamic setting, AI assistants become incredibly adept at understanding what you're trying to create. They can analyze your existing code, recognize the patterns you're using, and provide suggestions that feel like they're coming from a teammate who truly understands your project.

What really makes this partnership between AI and IDEs so effective is how effortlessly they come together to remove the usual hurdles that slow developers down. Instead of constantly bouncing between different tools and losing your focus, you can maintain your coding rhythm while the AI takes care of the repetitive tasks, like generating boilerplate code or suggesting the next logical step in your function. If you hit a snag or need to tidy up a messy section, you can just ask your AI assistant for help without ever stepping out of your development environment. It’s like having a savvy coding buddy right there with you, ready to lend a hand whenever you need it, making the whole software-building process feel more like a team effort and less like a lonely battle against complexity.

GitHub Copilot

Custom instructions in GitHub Copilot mark a game-changing shift in AI-assisted development, completely transforming how developers engage with artificial intelligence to write code. By letting developers set their own parameters and preferences, these instructions help Copilot grasp and adapt to the unique coding styles, architectural patterns, and project needs of each individual or team. This level of customization goes far beyond just filling in code; it covers everything from naming conventions and comment styles to preferred frameworks, libraries, and design patterns. The outcome is an AI assistant that doesn’t just churn out generic code but offers suggestions that seamlessly blend with existing codebases and adhere to established team standards. This tailored approach significantly cuts down the time developers spend fine-tuning AI-generated suggestions, as the output aligns more closely with their intended style right from the start.

The real perks of custom instructions go way beyond just making things easier; they actually lead to noticeable improvements in how development workflows run and the quality of the code. When Copilot gets a developer's go-to error handling methods, testing frameworks, and documentation styles, it can whip up more advanced and contextually fitting solutions that need little tweaking. This added relevance boosts productivity since developers can spend less time fine-tuning suggestions and more time tackling complex problems and unleashing their creativity in software development. 

Plus, custom instructions are a fantastic learning resource, especially for junior developers, as they get to see best practices consistently reflected in the AI-generated code. The debugging help also becomes more useful, as Copilot can propose fixes that fit right in with the project's established patterns and conventions, making the debugging process feel more intuitive and manageable. This all-encompassing approach to AI customization ultimately fosters a smoother collaboration between human creativity and artificial intelligence, leading to cleaner, more consistent codebases and more efficient development cycles.

GitHub Copilot is a smart coding assistant powered by AI, created by GitHub in collaboration with OpenAI. It sits right inside your code editor, helping you with code completion. This tool is built on cutting-edge language models that have learned from billions of lines of public code. Copilot understands the context of your writing and offers real-time suggestions for everything from entire functions to complex algorithms, supporting a wide range of programming languages like Python, JavaScript, TypeScript, Ruby, Go, and more. It fits right in with popular development environments such as Visual Studio Code, Visual Studio, Neovim, and JetBrains IDEs, providing suggestions as you type. These can be anything from simple autocomplete to more advanced implementations of intricate logic based on your comments or partial code. Beyond just generating code, Copilot can assist with writing tests, creating documentation, explaining existing code, and even translating code between different languages. This makes it a versatile tool that boosts productivity for developers of all skill levels, while also helping them learn new patterns and best practices through its AI-driven suggestions.

Let's deep dive into getting GitHub Copilot onto Visual Studio Code (VS Code) with custom instructions.

Prerequisites

• VS Code is installed on your machine. If not, download from here.
• Install the plugins GitHub Copilot and GitHub Copilot Chat. You can check the instructions here. 
• From the bottom right corner of VS Code, click on the GitHub Copilot icon to log in to GitHub and subsequently authorize VS Code to use GitHub Copilot.

Enabling Custom Instructions

By default, VS Code enables an option to use custom instructions within settings. Cross-verify them by opening user settings in VS Code, which should be like below.

Creating Instruction Files

There are two ways to create instruction files:

1. .github/copilot-instructions.md: a single instruction file that contains all the instructions for your workspace. These instructions are automatically included in every chat request. More information can be found here.
   • Good fit for simple applications, the downside would be that the model needs to read all the instructions present.
2. .instructions.mdfiles: one or more prompt files that contain custom instructions for specific tasks. You can attach individual prompt files to a chat request, or you can configure them to be automatically included for specific files or folders. More information can be found here.
   • Good approach for large applications where we can ask the model to choose and follow the correct instructions.
   • Use applyTo property in the instruction file header to automatically apply the instructions to specific files or folders. More on configuration can be found here.

Generating Instruction Files Using Prompts from Workspace

GitHub Copilot can generate instructions from any workspace/project which is already been created. The only thing needed is a best prompt for it to create the required file(s) in the designated folders.

Single Instruction File

Prompt: Create .github/copilot-instructions.md by reading the current workspace.

With this prompt, Copilot will create an instruction file that can be referenced while generating code/documentation.

Multiple Instruction Files

Prompt: Create instruction files in .github/instructions folder with suffix as .instructions.md by reading the current workspace for common, Java, React, database, security, deployment components. 

With this prompt, Copilot will create a bunch of instruction files depending on the list provided in the prompt. The key here is applyTo in the instruction files to make sure which file needs to be used when. For example, for all Java-related instructions files, the applyTo will have *.java as below.

---

applyTo: "**/*.java"

---

- Use Java 8+ and Spring Boot for backend development.
- Organize code into `controller`, `service`, `repository`, `beans`, `security`, and `util` packages.
- Use dependency injection for all services and repositories.

Final Words

AI code assistants really shine when they're integrated into an IDE. They make the development process smoother by creating a structured and interactive coding environment. When you combine AI-driven suggestions with key IDE features like syntax highlighting, error detection, and version control, developers can really boost their efficiency. Take GitHub Copilot in Visual Studio Code, for example, it simplifies coding workflows by providing smart completions and custom instructions that fit the project's needs. 

By properly setting up Copilot with instruction files and prompts tailored to the workspace, developers can tap into AI for smarter automation and better coding standards. Embracing these AI-powered tools within an IDE allows developers to concentrate on creativity and problem-solving, all while cutting down on those tedious, repetitive tasks.

Happy learning and happy coding!