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.
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!
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.
Learn how we’re experimenting with open source AI models to systematically incorporate customer feedback to supercharge our product roadmaps.
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.
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.
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.
Here’s how SAST tools combine generative AI with code scanning to help you deliver features faster and keep vulnerabilities out of code.
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.
Learn how your organization can customize its LLM-based solution through retrieval augmented generation and fine-tuning.
Explore the capabilities and benefits of AI code generation, and how it can improve the developer experience for your enterprise.
Learn how we’re experimenting with generative AI models to extend GitHub Copilot across the developer lifecycle.
Here’s everything you need to know to build your first LLM app and problem spaces you can start exploring today.
Explore how LLMs generate text, why they sometimes hallucinate information, and the ethical implications surrounding their incredible capabilities.
Open source generative AI projects are a great way to build new AI-powered features and apps.
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.
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.
Build what’s next on GitHub, the place for anyone from anywhere to build anything.
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