An AI generated image from ImageFX of a female African American judge reading a very long printout with ethical language, and making an ethical judgment. Style: flat illustration. Color: White, some black, some maroon, some gold.

CreateAI Support Services

AI Upskilling Friday Office Hour (Virtual)

Join us every Friday from 12–1 PM (AZ time) on Zoom:

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 / Beta Testing Group.

Interested in becoming a CreateAI Beta user or interested in joining our Beta testing sessions? Let us know and we’d love to have you.

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

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.