Update aspnetcore/tutorials/ai-powered-group-chat/ai-powered-group-chat.md

Co-authored-by: David Pine <[email protected]>

Author: wadepickett

aspnetcore/tutorials/ai-powered-group-chat/ai-powered-group-chat.md

@@ -93,6 +93,6 @@ if (totalCompletion.Length - lastSentTokenLength > 20)
 
 This project opens up exciting possibilities for further enhancement:
 1. **Advanced AI features**: Leverage other OpenAI capabilities like sentiment analysis, translation, or summarization. 
-2. **Incorporating multiple AI agents**: You can introduce multiple AI agents with distinct roles or expertise areas within the same chat. For example, one agent might focus on text generation, the other provides image or audio generation. This can create a richer and more dynamic user experience where different AI agents interact seamlessly with users and each other.
-3. **Share chat history between server instances**: Implement a database layer to persist chat history across sessions, allowing conversations to resume even after a disconnect. Beyond SQL or NO SQL based solutions, you can also explore using a caching service like Redis. It can significantly improve performance by storing frequently accessed data, such as chat history or AI responses, in memory. This reduces latency and offloads database operations, leading to faster response times, particularly in high-traffic scenarios. 
-4. **Leveraging Azure SignalR Service**: [Azure SignalR Service](https://learn.microsoft.com/azure/azure-signalr/signalr-overview) provides scalable and reliable real-time messaging for your application. By offloading the SignalR backplane to Azure, you can scale out the chat application easily to support thousands of concurrent users across multiple servers. Azure SignalR also simplifies management and provides built-in features like automatic reconnections.
+1. **Incorporating multiple AI agents**: You can introduce multiple AI agents with distinct roles or expertise areas within the same chat. For example, one agent might focus on text generation, the other provides image or audio generation. This can create a richer and more dynamic user experience where different AI agents interact seamlessly with users and each other.
+1. **Share chat history between server instances**: Implement a database layer to persist chat history across sessions, allowing conversations to resume even after a disconnect. Beyond SQL or NO SQL based solutions, you can also explore using a caching service like Redis. It can significantly improve performance by storing frequently accessed data, such as chat history or AI responses, in memory. This reduces latency and offloads database operations, leading to faster response times, particularly in high-traffic scenarios. 
+1. **Leveraging Azure SignalR Service**: [Azure SignalR Service](/azure/azure-signalr/signalr-overview) provides scalable and reliable real-time messaging for your application. By offloading the SignalR backplane to Azure, you can scale out the chat application easily to support thousands of concurrent users across multiple servers. Azure SignalR also simplifies management and provides built-in features like automatic reconnections.