Elvis Saravia's Open-Source AI Education

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Elvis Saravia built the internet’s go-to reference for prompt engineering. His Prompt Engineering Guide has nearly 70,000 GitHub stars and gets cited in countless tutorials and corporate training materials. But the guide is just one piece of a larger mission: making AI knowledge accessible to everyone.

From Meta AI to DAIR.AI

Saravia holds a PhD and spent years at Meta AI working with the Papers with Code, PyTorch, and FAIR teams. He saw firsthand how AI knowledge remained locked inside research labs and big tech companies. In 2020, he founded DAIR.AI (Democratizing Artificial Intelligence Research, Education, and Technologies) to change that.

The organization’s mission sounds idealistic, but the execution is practical. DAIR.AI produces concrete, usable resources: annotated paper collections, course notes, visual templates for ML diagrams, and hands-on tutorials.

The Open-Source Portfolio

Saravia’s GitHub organization hosts several high-value repositories:

The consistency matters. These aren’t abandoned side projects—they receive regular updates and community contributions.

Teaching Prompting Like a Discipline

The Prompt Engineering Guide treats prompting as a real skill with learnable techniques. The content spans:

Saravia avoids hype. The guide includes honest discussions about model limitations, hallucinations, and when prompting techniques fail. This practical grounding is why developers actually trust it.

The Newsletter and Academy

Beyond open-source, Saravia runs an AI newsletter with over 40,000 subscribers. The format is consistent: weekly summaries of top papers, tools, and techniques. No breathless announcements about the latest model—just useful information delivered reliably.

The DAIR.AI Academy offers paid courses on specific topics: prompt engineering fundamentals, building AI agents with n8n, RAG systems, and recently, Claude Code workflows. The courses bridge the gap between free documentation and hands-on implementation.

Philosophy: Open Access, Practical Focus

Saravia’s approach has a few consistent threads:

Start with what exists. Rather than building proprietary tools, he curates and explains what’s already available. ML-YouTube-Courses doesn’t create new videos—it helps people find the good ones that already exist.

Document the real workflow. The Prompt Engineering Guide includes sections on debugging prompts, evaluating outputs, and handling edge cases. This is the stuff practitioners actually need but rarely find in official docs.

Update continuously. AI moves fast, and Saravia’s resources stay current. The Prompt Engineering Guide added context engineering sections as the concept emerged in 2025. The papers-of-the-week series captures research as it happens.

Make it reusable. The ML-Visuals project lets anyone use professional-quality diagrams in their own work. The course notes are shared openly for others to build on.