Understanding how GitHub Copilot works
Published Jan 24 2024 12:43 AM 6,343 Views
Microsoft
A while back, a friend contacted me seeking assistance with debugging Python code. Being a burgeoning Cyber Security Analyst, they required guidance in crafting a basic HTTP server in Python. Specifically, the server needed to listen on port 8000 and serve an index.html file upon accessing the root path. Unfortunately, my expertise in Python was limited; while I had used it a few times in the past, I wasn't proficient enough to help. Immediately, I turned to my AI friend - GitHub Copilot - for support. Remarkably, within just a few minutes of providing the prompt below, we obtained the accurate and updated code.

 

Create a simple HTTP server in Python. Use an if else statement and index.html as the root path.
What a relief! A few hours of brainstorming and trying out different methods and finally in less than 5 minutes our code had run successfully. Here's the response we got from GitHub Copilot:

 

HTTP Server 2.png

 

His excitement led to a question: "How was Copilot able to do this?" It dawned on me that this inquiry had surfaced previously and would likely arise again in the future. This was the perfect time to document this and share the explanation for everyone's benefit.

This article was curated based off this Microsoft Learn path - GitHub Copilot Fundamentals - Understand the AI Pair Programmer. Before diving in, I wanted to take some time to briefly talk about GitHub Copilot.

Introducing GitHub Copilot

GitHub Copilot utilizes the amazing capabilities of OpenAI's Codex model to suggest code and entire functions in real-time, right from your editor. OpenAI's Codex can handle coding tasks and is specifically optimized for programming languages and can understand, interpret, and generate code in multiple languages.

In a recent study conducted by GitHub and Microsoft, it was discovered that developers witnessed a remarkable surge in productivity while employing GitHub Copilot for real-world projects and tasks. Astonishingly, within less than two years since its launch, developers reported the following remarkable outcomes while utilizing GitHub Copilot:
  • An astounding 46% of new code is now authored by AI.
  • Overall developer productivity surged by 55%, leading to more efficient coding processes.
  • A whopping 74% of developers expressed a heightened sense of focus and engagement in their work tasks, indicating a more fulfilling and rewarding work experience.

GitHub Copilot is available as an extension for Visual Studio Code, Visual Studio, Vim/Neovim, and the JetBrains suite of integrated development environments (IDEs). To learn how to sign up, set up, configure and troubleshoot GitHub Copilot, check this link - Set up, configure and troubleshoot GitHub Copilot.

 

Let's talk about Prompt Engineering

GitHub Copilot utilizes something called Prompt Engineering. Imagine you're chatting with GitHub Copilot, your coding buddy. Now, prompt engineering is like teaching Copilot a secret language – a language that helps it understand your coding dreams and turn them into reality.
So, instead of just typing any old thing, you craft these cool prompts that give Copilot the lowdown on your coding desires. It's like whispering sweet coding nothings, and voila! Copilot gets it, cooks up some genius-level code, and serves it to you on a digital silver platter.
Prompt engineering is your ticket to unleashing the full power of Copilot, turning your coding sessions into a dynamic dance of creativity and collaboration. So, get ready to sprinkle some coding magic with your prompts and watch Copilot bring your wildest coding dreams to life!

Prompt Engineering is the process of crafting clear instructions to guide AI systems like GitHub Copilot to generate context-appropriate code. This ensures that code is syntactically, functionally, and contextually correct. To learn more about Prompt Engineering and craft better prompts, check out this module from Microsoft - Prompt engineering foundations and best practices. As a summary, we should ensure that our prompts are clear and specific, provide enough context and examples.
 
GitHub Copilot learns and generates code based off your prompts. To enhance its understanding of specific code contexts, you might need to provide it with examples. It could be just one example and in other cases, you might need to provide it with multiple examples. Whichever you decide to go with, GitHub Copilot uses your prompts and examples (if provided) to suggest code for you.
 

So how does GitHub Copilot work?

GitHub Copilot utilizes the prompts entered by the user to provide smart, usable code. Here's how it does it.

 

Illustration of Copilot Prompt Processing FlowIllustration of Copilot Prompt Processing Flow

 

 

Spoiler
To be able to use GitHub Copilot, you would need to install the extension. This guide shows you how to do so - Getting started with GitHub Copilot.

Ensuring Secure Prompt Transmission and Contextual Understanding

It all begins within your chosen code editor. Your prompts are securely transmitted via HTTPS, guaranteeing the confidentiality of your submissions, whether conveyed as comments within your code or through the Copilot chat. This secure transmission safeguards all your sensitive data as it travels to GitHub Copilot's servers.

Upon receipt, GitHub Copilot assesses the code both preceding and following the cursor position to grasp the complete context of the prompt. Subsequently, it scrutinizes the filename and file type of the edited document, enabling tailored code suggestions aligned with the specific file format. Additionally, it gathers information about other open tabs, ensuring that the generated code complements other segments within the same project.

Content filtering

Prior to analyzing context, extracting intent, and generating code, GitHub Copilot prioritizes the filtration of personal data to safeguard user privacy and data security. Additionally, it scrutinizes content to identify and eliminate hate speech and inappropriate material. Through specialized algorithms, Copilot actively detects and prevents the generation of hate speech, offensive language, or any inappropriate content that could potentially cause harm or offense.
 

Analyze Context

Copilot leverages the compiled context data to contextualize the user's prompt. This process assists Copilot in comprehending the significance of the prompts and the specific coding task they aim to resolve. Upon completion, it proceeds to extract the intent embedded within the prompt by identifying keywords, phrases, and contextual clues. Next, Copilot maps these extracted intents to distinct coding actions or functionalities. Following this, it translates the user's overarching request into a tangible coding task, bridging the gap between high-level intent and concrete implementation.

Code Generation

Guided by the identified intent, Copilot presents tailored code suggestions that align with your coding preferences. It adeptly utilizes appropriate functions and variable names that suit the specific scenario. While crafting code blocks, Copilot ensures accuracy in syntax and context, aligning seamlessly with the project's language, framework, and established standards. Copilot adheres to personalized settings, including coding styles and constraints, if specified.

User Interaction

Alright, now comes the exciting part – you get to see the code that Copilot has generated for you! Here's the best part: you have the power to choose what to do with it. You can either give it a thumbs-up and use the code as is, tweak it to your liking, or give it a pass and reject the suggestions altogether. As the captain of this coding journey, you're in the driver's seat, deciding how things unfold.

Feedback Loop Initiation

Imagine a platform without feedback like a chef without tasting their cooking - it just doesn't fly! Just like any tool, Copilot thrives on feedback. When you accept its code suggestions, it's like giving it a high-five, saying, "Good job!" But here's the fun part: Copilot loves learning and gets better by soaking in the changes you make to its suggestions or when you say, "Nah, that won't work." So, every time you tweak or reject its ideas, you're helping Copilot level up its coding game!

Prompt History Retention

During your coding adventures, Copilot keeps a record of your prompts, context details, and interactions. This record acts like a memory bank, helping Copilot give you suggestions that stay on track and make sense with what you're working on.

Repeat for Subsequent Prompts

It's like a dance! You keep giving Copilot prompts, and it keeps grooving, understanding what you want and busting out code moves. As you two keep boogieing together, Copilot learns from your prompts and interactions, getting better at predicting what you need and fine-tuning its code-making skills.

GitHub Copilot isn't just a tool; it's a coding wizard! This incredible AI marvels at context, extracting intents, and generates code across various programming languages like a maestro. Its superpowers don't stop there – they supercharge developer productivity, foster learning, and smoothen the coding journey. Picture this: Copilot swoops in with real-time help, crafting code that fits like a glove and meets the coding gold standard. This wizardry frees up developers to focus on the thrilling bits of innovation and problem-solving, turbocharging the development process.

GitHub Copilot isn't just another tool; it's a game-changer in the coder's toolkit, revolutionizing workflows and bringing a spark of AI magic to the creative coding realm. As this wondrous AI continues to evolve, brace yourself for more excitement, innovation, and a whole new level of coding awesomeness in the future!

Additional Resources

  1. Learn more about GitHub Copilot here
  2. Develop with GitHub Copilot and Visual Studio Code
  3. Set up, configure, and troubleshoot GitHub Copilot
  4. GitHub Copilot user prompt process flow
1 Comment
Co-Authors
Version history
Last update:
‎Jan 24 2024 12:47 AM
Updated by: