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

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.

How GitHub harnesses AI to transform customer feedback into action

Learn how we’re experimenting with open source AI models to systematically incorporate customer feedback to supercharge our product roadmaps.

Latest

What are AI agents and why do they matter?

Learn how AI agents and agentic AI systems use generative AI models and large language models to autonomously perform tasks on behalf of end users.

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

What’s new with GitHub Copilot: July 2024

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.

Unlocking the power of unstructured data with RAG

Unstructured data holds valuable information about codebases, organizational best practices, and customer feedback. Here are some ways you can leverage it with RAG, or retrieval-augmented generation.

How AI enhances static application security testing (SAST)

Here’s how SAST tools combine generative AI with code scanning to help you deliver features faster and keep vulnerabilities out of code.

What is retrieval-augmented generation, and what does it do for generative AI?

Here’s how retrieval-augmented generation, or RAG, uses a variety of data sources to keep AI models fresh with up-to-date information and organizational knowledge.

Customizing and fine-tuning LLMs: What you need to know

Learn how your organization can customize its LLM-based solution through retrieval augmented generation and fine-tuning.

How AI code generation works

Explore the capabilities and benefits of AI code generation, and how it can improve the developer experience for your enterprise.

How we’re experimenting with LLMs to evolve GitHub Copilot

Learn how we’re experimenting with generative AI models to extend GitHub Copilot across the developer lifecycle.

Flow chart that reads from right to left, showing components of a large language model application and how they all work together.

The architecture of today’s LLM applications

Here’s everything you need to know to build your first LLM app and problem spaces you can start exploring today.

Demystifying LLMs: How they can do things they weren’t trained to do

Explore how LLMs generate text, why they sometimes hallucinate information, and the ethical implications surrounding their incredible capabilities.

A developer’s guide to open source LLMs and generative AI

Open source generative AI projects are a great way to build new AI-powered features and apps.

How to build an enterprise LLM application: Lessons from GitHub Copilot

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

A guide to designing and shipping AI developer tools

GitHub’s design experts share 10 tips and lessons for designing magical user experiences for AI applications and AI coding tools.

An abstract image of the moon representing LLMs.

A developer’s guide to prompt engineering and LLMs

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.

Inside GitHub: Working with the LLMs behind GitHub Copilot

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.