Generative AI Tool Pre-Release Evaluation Guide

Arizona State University remains dedicated to responsible, principled innovation when deploying generative AI solutions, including chatbots. This guide ensures each project aligns with ASU’s values by mitigating potential risks—such as misinformation, bias, toxicity, and compliance lapses—using rigorous methods like automated testing, red teaming, and pilot experiments. In doing so, we uphold accuracy, fairness, and user trust while enhancing digital experiences across the university.

Agents in Generative AI

Agents in generative AI are semi-autonomous entities that collaborate and interact dynamically, allowing them to solve complex problems and combine specialized capabilities for greater efficiency and adaptability.

How to Use Knowledge Base (RAG)

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

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 Model Comparison, ASU GPT, and MyAI Builder (access request required). Updated: March 25, 2025. 

Breakdown of RAG Model Parameters, Settings and Their Impact

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.