GitHub engineers and industry thought leaders offer tips, best practices, and practical explainers about various aspects of AI and ML, ranging from fundamental concepts to advanced techniques and real-world applications. For more detailed documentation and practical guides on GitHub’s own AI coding tool, GitHub Copilot, check out GitHub’s official documentation.
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.
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.
Explore how generative AI coding tools are changing the way developers and companies build software.
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.
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.
This post features a guest interview with Diego M. Oppenheimer, CEO at Algorithmia Over the past few years, machine learning has grown in adoption within the enterprise. More organizations are…
Background Machine Learning Operations (or MLOps) enables Data Scientists to work in a more collaborative fashion, by providing testing, lineage, versioning, and historical information in an automated way. Because the…
To make language detection more robust and maintainable in the long run, we developed a machine learning classifier named OctoLingua based on an Artificial Neural Network (ANN) architecture which can handle language predictions in tricky scenarios.
Our machine learning scientists have been researching ways to enable the semantic search of code.
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