CreateAI Office Hours Details: Get Help and Share Feedback
Wednesdays: Beta Testing & Early Feedback (12–1 PM AZ time)
Wednesdays are dedicated to our beta testers , a group of builders who help us shape the future of CreateAI. In these sessions, testers:
- Try out new features before they launch
- Share feedback and report bugs directly to the CreateAI team
- Influence what improvements go into production
Everyone is welcome to join these sessions, but the focus will be on beta testing and product feedback. Join Wednesday Beta Office Hours
Fridays: General Support & Community Help (12–1 PM AZ time)
Fridays are open to the entire ASU CreateAI community. These sessions are perfect for anyone who wants to:
- Get help with CreateAI projects
- Learn tips and best practices
- Connect with peers and see how others are using CreateAI
Troubleshoot issues in real time with the support team
If you’re new to CreateAI or just need a space to ask questions, Friday office hours are for you.
Why Office Hours Matter
CreateAI is a community-driven platform. Your ideas, feedback, and questions are what help us improve. By joining office hours, you’re not only getting support, but also helping shape the future of AI at ASU.
We look forward to seeing you at our next session whether it’s Wednesdays for beta testing or Fridays for general support.
Keep Reading
How to Use Knowledge Base (RAG)
Jinjing Zhao
Explore an overview of the Knowledge Base and Retrieval Augmented Generation (RAG) methods. Learn about the different types of Knowledge Base retrieval and understand the distinctions between the Knowledge Base and system prompts.
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.