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.
We surveyed 500 U.S.-based developers at companies with 1,000-plus employees about how managers should consider developer productivity, collaboration, 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.
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.
Explore how generative AI coding tools are changing the way developers and companies build software.
GitHub CEO Thomas Dohmke demonstrated the power of GitHub Copilot X live on stage.
Rapid advancements in generative AI coding tools like GitHub Copilot are accelerating the next wave of software development. Here’s what you need to know.
Generative AI has been dominating the news lately—but what exactly is it? Here’s what you need to know, and what it means for developers.
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