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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 .

Latest

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.Klint FinleyNovember 14, 2024
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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!Chris Reddington, Cole BemisOctober 9, 2024
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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.Juan Pablo Flores CortésOctober 3, 2024
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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.Aaron WinstonAugust 13, 2024
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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.Elle Shwer, Stella YangJuly 31, 2024
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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.Mariana BorgesJuly 30, 2024
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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.Nicole ChoiJune 13, 2024
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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.Nicole ChoiMay 9, 2024
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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.Nicole ChoiApril 4, 2024
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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.Nicole ChoiFebruary 28, 2024
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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.Jeimy RuizFebruary 22, 2024
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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.Sara VerdiDecember 6, 2023
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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.Nicole ChoiOctober 30, 2023
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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.Jeimy RuizOctober 27, 2023
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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.Gwen DavisOctober 5, 2023
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