Remove RAI references

Author: marktayl1

README.md

@@ -41,7 +41,7 @@ This application is an AI-driven orchestration system that manages a group of AI
 - Incorporate human feedback into the workflow.
 - Maintain state across sessions with persistent storage.
 
-This system is intended for developing and deploying custom AI solutions for specific customers. This code has not been tested as an end-to-end, reliable, RAI compliant production application- it is a foundation to help accelerate building out multi-agent systems. You are encouraged to add your own data and functions to the agents, and then you must apply your own performance and safety evaluation testing frameworks to this system before deploying it.
+This system is intended for developing and deploying custom AI solutions for specific customers. This code has not been tested as an end-to-end, reliable production application- it is a foundation to help accelerate building out multi-agent systems. You are encouraged to add your own data and functions to the agents, and then you must apply your own performance and safety evaluation testing frameworks to this system before deploying it.
 
 \
 ![image](./documentation/images/readme/macae-application.png)

documentation/CustomizeSolution.md

@@ -1,6 +1,6 @@
 # Accelerating your own Multi-Agent -Custom Automation Engine MVP
 
-As the name suggests, this project is designed to accelerate development of Multi-Agent solutions in your environment.  The example solution presented shows how such a solution would be implemented and provides example agent definitions along with stubs for possible tools those agents could use to accomplish tasks.  You will want to implement real functions in your own environment, to be used by agents customized around your own use cases. Users can choose the LLM that is optimized for responsible use. The default LLM is GPT-4o which inherits the existing RAI mechanisms and filters from the LLM provider. We encourage developers to review [OpenAI’s Usage policies](https://openai.com/policies/usage-policies/) and [Azure OpenAI’s Code of Conduct](https://learn.microsoft.com/en-us/legal/cognitive-services/openai/code-of-conduct) when using GPT-4o. This document is designed to provide the in-depth technical information to allow you to add these customizations. Once the agents and tools have been developed, you will likely want to implement your own real world front end solution to replace the example in this accelerator.
+As the name suggests, this project is designed to accelerate development of Multi-Agent solutions in your environment.  The example solution presented shows how such a solution would be implemented and provides example agent definitions along with stubs for possible tools those agents could use to accomplish tasks.  You will want to implement real functions in your own environment, to be used by agents customized around your own use cases. Users can choose the LLM that is optimized for responsible use. The default LLM is GPT-4o which inherits the existing responsible AI mechanisms and filters from the LLM provider. We encourage developers to review [OpenAI’s Usage policies](https://openai.com/policies/usage-policies/) and [Azure OpenAI’s Code of Conduct](https://learn.microsoft.com/en-us/legal/cognitive-services/openai/code-of-conduct) when using GPT-4o. This document is designed to provide the in-depth technical information to allow you to add these customizations. Once the agents and tools have been developed, you will likely want to implement your own real world front end solution to replace the example in this accelerator.
 
 ## Technical Overview
 
@@ -12,7 +12,7 @@ This application is an AI-driven orchestration system that manages a group of AI
 - Incorporate human feedback into the workflow.
 - Maintain state across sessions with persistent storage.
 
-This code has not been tested as an end-to-end, reliable, RAI compliant production application- it is a foundation to help accelerate building out multi-agent systems. You are encouraged to add your own data and functions to the agents, and then you must apply your own performance and safety evaluation testing frameworks to this system before deploying it.
+This code has not been tested as an end-to-end, reliable production application- it is a foundation to help accelerate building out multi-agent systems. You are encouraged to add your own data and functions to the agents, and then you must apply your own performance and safety evaluation testing frameworks to this system before deploying it.
 
 Below, we'll dive into the details of each component, focusing on the endpoints, data types, and the flow of information through the system.