GitHub Copilot

GitHub Copilot Chat can help developers create prototypes, understand code, make UI changes, troubleshoot errors, make code more accessible, and generate unit tests.

All GitHub Copilot for Individuals users now have access to GitHub Copilot Chat beta, bringing natural language-powered coding to every developer in all languages.

The team behind GitHub Copilot shares its lessons for building an LLM app that delivers value to both individuals and enterprise users at scale.

Rust continues to top the charts as the most admired and desired language by developers, and in this post, we dive a little deeper into how (and why) Rust is stealing the hearts of developers around the world.

Today, we’re announcing a private beta of GitHub Copilot with code referencing that includes a filter to detect code suggestions matching public code on GitHub.

We’re thrilled to announce two major updates to GitHub Copilot code Completion’s capabilities that will help developers work even more efficiently and effectively.

In this step-by-step tutorial, you will learn how to use GitHub Copilot to build an application with OpenAI’s gpt-3.5-turbo model.

We’re launching the GitHub Copilot Trust Center to provide transparency about how GitHub Copilot works and help organizations innovate responsibly with generative AI.

All GitHub Copilot for Business users now have access to a limited GitHub Copilot Chat beta, bringing the power of conversational coding right to the IDE.

Prompt engineering is the art of communicating with a generative AI model. In this article, we’ll cover how we approach prompt engineering at GitHub, and how you can use it to build your own LLM-based application.

Today at Collision Conference we unveiled breaking new research on the economic and productivity impact of generative AI–powered developer tools. The research found that the increase in developer productivity due to AI could boost global GDP by over $1.5 trillion.

In this prompt guide for GitHub Copilot, two GitHub developer advocates, Rizel and Michelle, will share examples and best practices for communicating your desired results to the AI pair programmer.

Developers behind GitHub Copilot discuss what it was like to work with OpenAI’s large language model and how it informed the development of Copilot as we know it today.

With a new Fill-in-the-Middle paradigm, GitHub engineers improved the way GitHub Copilot contextualizes your code. By continuing to develop and test advanced retrieval algorithms, they’re working on making our AI tool even more advanced.

Here’s how, in seven steps, I built my first browser extension with GitHub Copilot—and my three major takeaways about learning and pair programming in the age of AI.