AI & ML

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.

Featured

What the heck is MCP and why is everyone talking about it?

Everyone’s talking about MCP these days when it comes to large language models (LLMs)—here’s what you need to know.

GitHub for Beginners: How to get LLMs to do what you want

Learn how to write effective prompts and troubleshoot results in this installment of our GitHub for Beginners series.

Mastering GitHub Copilot: When to use AI agent mode

Discover the differences between agent mode and Copilot Edits with GitHub Copilot—and when to use them in your workflows.

GitHub for Beginners: Essential features of GitHub Copilot

Get the most out of Copilot with code completion, inline chat, slash commands, Copilot code review, and more.

Latest

Not just for developers: How product and security teams can use GitHub Copilot

GitHub Copilot isn’t just for developers! Discover how product managers, security professionals, scrum masters, and more use GitHub Copilot to streamline tasks, automate workflows, and boost productivity across teams.

GitHub for Beginners: How to get started with GitHub Copilot

Get started with GitHub Copilot and navigate features like Copilot Chat in this installment of the GitHub for Beginners series.

How to debug code with GitHub Copilot

GitHub Copilot can streamline your debugging process by troubleshooting in your IDE, analyzing pull requests, and more, helping you tackle issues faster and more robustly.

A thumbnail graphic for GitHub Copilot, an AI coding tool from GitHub.

How to refactor code with GitHub Copilot

Discover how to use GitHub Copilot to refactor your code and see samples of it in action.

Open source AI is already finding its way into production

Open source AI models are in widespread use, enabling developers around the world to build custom AI solutions and host them where they choose.

Modernizing legacy code with GitHub Copilot: Tips and examples

Learn how to modernize legacy code with GitHub Copilot with real-world examples.

How we evaluate AI models and LLMs for GitHub Copilot

We share some of the GitHub Copilot team’s experience evaluating AI models, with a focus on our offline evaluations—the tests we run before making any change to our production environment.

Documenting and explaining legacy code with GitHub Copilot: Tips and examples

Learn how to document and explain legacy code with GitHub Copilot with real-world examples.

How to use GitHub Copilot: What it can do and real-world examples

How Copilot can generate unit tests, refactor code, create documentation, perform multi-file edits, and much more.

So many tokens, so little time: Introducing a faster, more flexible byte-pair tokenizer

We released a new open source byte-pair tokenizer that is faster and more flexible than popular alternatives.

How to generate unit tests with GitHub Copilot: Tips and examples

Learn how to generate unit tests with GitHub Copilot and get specific examples, a tutorial, and best practices.

How developers spend the time they save thanks to AI coding tools

Developers tell us how GitHub Copilot and other AI coding tools are transforming their work and changing how they spend their days.

5 tips and tricks when using GitHub Copilot Workspace

GitHub Next launched the technical preview for GitHub Copilot Workspace in April 2024. Since then, we’ve been listening to the community, learning, and have some tips to share on how to get the most out of it!

How students teamed up to decode 2,000-year-old texts using AI

Students used GitHub Copilot to decode ancient texts buried in Mount Vesuvius, achieving a groundbreaking historical breakthrough. This is their journey, the technology behind it, and the power of collaboration.