Explore comprehensive guides about GitHub Copilot, an AI-powered code completion tool, to enhance coding efficiency and accuracy with best practices, tips for maximizing Copilot’s capabilities, and real-world examples. Want more technical information on GitHub Copilot? Head over to our documentation to learn more about using GitHub Copilot in your IDE, on GitHub, and across your workflows.
To enhance your coding experience, AI tools should excel at saving you time with repetitive, administrative tasks, while providing accurate solutions to assist developers. Today, we’re spotlighting three updates designed to increase efficiency and boost developer creativity.
The team behind GitHub Copilot shares its lessons for building an LLM app that delivers value to both individuals and enterprise users at scale.
GitHub’s design experts share 10 tips and lessons for designing magical user experiences for AI applications and AI coding tools.
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.
GitHub Copilot boosts developer productivity, but using it responsibly still requires good developer and DevSecOps practices.
We’re launching new improvements to GitHub Copilot to make it more powerful and more responsive for developers.
We will begin to introduce several new capabilities to GitHub Copilot in 2023 to continue delivering responsible innovation and true happiness at the keyboard.
GitHub Copilot: Parrot or Crow? A first look at rote learning in GitHub Copilot suggestions.
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