CreateAI Support Services
AI Upskilling Friday Office Hour (Virtual)
Join us every Friday from 12–1 PM (AZ time) on Zoom:
- Zoom link:
https://asu.zoom.us/j/87204740314
We encourage you to register (we coordinate attendance with the LX team):
Eventbrite: https://www.eventbrite.com/e/ai-upskilling-office-hours-tickets-1113129132199?aff=odcleoeventsincollection
You’re always welcome to join directly via Zoom as well. The Eventbrite page also includes additional AI learning opportunities you may be interested in.
We’ll have a dedicated breakout room for CreateAI users please, bring your questions, ideas, or projects!
CreateAI Beta Users Office Hour (Virtual)
Join us every Wednesday from 12–1 PM (AZ time):
- Zoom link:
https://asu.zoom.us/j/89092293944
Interested in becoming a CreateAI Beta user? Let us know and we’d love to have you.
If Fridays don’t work for you, you’re still welcome to join Wednesdays with your questions. Our team will be there to support.
In-Person Support (Tempe Campus)
Visit us at the Tech Hub, Creativity Commons:
Mondays: 12:00–1:00 PM and 2:00–3:00 PM
Tuesdays: 12:00–1:00 PM and 2:00–3:00 PM
Please check in at the front desk when you arrive.
Personalized 1:1 Consultations
Need deeper support?
We offer tailored 1:1 consultations to help you get the most out of CreateAI.
Book a session to:
- Refine your AI use case
- Optimize your Knowledge Base
- Review governance or privacy questions
- Get hands-on support with your project
We look forward to connecting with you, virtually, in person, or one-on-one.
Keep Reading
CreateAI Platform Available LLM Models
Faith Timoh Abang
We are proud to offer 40+ models including multi-modal (voice, image, text) for the ASU community to access securely on the CreateAI Platform. Users can find the following models available for experimentation and use in CreateAI Compare, CreateAI Chat, and CreateAI Builder (access request required). Originally posted: January 1, 2025.
Breakdown of RAG Model Parameters, Settings and Their Impact
Kofi Wood
Retrieval-Augmented Generation (RAG) is an advanced approach in natural language processing that integrates information retrieval and generative language modeling. Unlike traditional language models that generate responses solely based on their pre-trained knowledge, RAG combines retrieval mechanisms with generative models to enhance the relevance and accuracy of its responses. This hybrid framework works by first retrieving relevant documents or information from a predefined knowledge base (e.g., databases, documents, or PDFs) and then using a generative model (such as a transformer-based model) to synthesize a response that incorporates the retrieved context.