FORC: J-Term Data Skills Immersion Training for Graduate Students Track 2 In-Person
Description
NYU Data Services (NYU Libraries and IT), the Graduate School of Arts & Science Master’s College, and the Arts & Sciences Office of Teaching Excellence and Innovation are excited to launch a new extension of the Foundations of Research Computing (FORC) Camp training opportunity for graduate students! Given the strong interest in data skills development on the part of our students and the success of the summer FORC Camp offerings, we will pilot a JTerm offering of two new tracks. Grounded in the principle that foundational data skills are essential for early career researchers, these two new tracks will explore the potential of generative AI as a research tool.
During this one day in-person learning opportunity, taking place January 16, 2025, interested students can select one of two tracks.
The day’s schedule will include four hours of interactive instruction. Participants who complete all hours of the FORC curriculum will receive a letter of completion for their portfolio detailing the skills covered in their track.
- Date:
- Thursday, January 16, 2025
- Time:
- 10:00am - 3:00pm
- Time Zone:
- Eastern Time - US & Canada (change)
- Location:
- Bobst Library, 7th Floor, Rm. 743
- Libraries:
- Bobst Library
- Type:
- Data Services
Track 2:
Schedule
| Date | Time | Room |
| January 16 | 10am - 12pm | Bobst 743 |
| 1pm - 3pm | Bobst 743 |
Description
In this track, users will learn about Retrieval Augmented Generation (RAG) and how it enhances AI models by combining external data retrieval with large language models (LLMs). They will explore the steps to build a RAG pipeline, including embedding text into vector representations, retrieving relevant context from databases, and augmenting prompts to generate accurate answers. The tutorial provides practical insights into when to use RAG over fine-tuning models and how to integrate this approach in building dynamic, context-aware AI solutions.
For additional information, see Retrieval Augmented Generation (RAG) page.
Prerequisites: basic understanding of python
