Introduction to Retrieval Augmented Generation (RAG)

Duke University, located in Durham, United States, is a historic institution founded in 1838. As an international student, you'll find many advantages to studying here, including access to a diverse community of 3584 international students, a broad academic offering, and a unique blend of tradition and innovation. Specifically, you'll benefit from the university's strong research focus and its location in the heart of North Carolina's Research Triangle.

Duke University offers 213 programs, with many focusing on engineering, business, and medicine. The university is particularly renowned for its departments of engineering and environmental sciences, which provide opportunities for you to engage in cutting-edge research. English is the primary language of instruction, but you'll also have the chance to learn other languages, such as Spanish, Chinese, and Arabic, through the university's language programs.

As a student, you'll have access to modern facilities, including libraries, labs, and recreational centers. The campus is situated in a charming city with a rich cultural scene, offering many opportunities for you to explore and engage with the local community. For instance, you can visit the nearby Durham Performing Arts Center or take part in the city's annual festivals, which celebrate music, food, and art. With many student clubs and organizations available, you'll find plenty of ways to get involved and make the most of your time at Duke University.

The Introduction to Retrieval Augmented Generation (RAG) program at Duke University is a non-degree course that teaches students how to build a RAG system. This English-taught program is project-based and takes place over several weeks. It helps students learn how to import data, create embeddings, and build a RAG system using open source tools.

The curriculum covers hands-on topics such as importing data into Pandas, creating embeddings with SentenceTransformers, and building a RAG system with Qdrant and a large language model. Students will gain experience with open source tools and learn how to apply them to a powerful generative AI application.

After completing this program, students can pursue careers as AI Engineers, Data Scientists, Software Developers, Research Assistants, or Machine Learning Engineers. They can work in industries such as technology, research, or finance, and for employers like tech startups, universities, or consulting firms.