Update copilot-sdk-build-rag.md

remove deploy web app for now

Author: sdgilley

articles/ai-studio/tutorials/copilot-sdk-build-rag.md

@@ -15,11 +15,12 @@ ms.custom: copilot-learning-hub, ignite-2024
 
 # Tutorial:  Part 2 - Build a custom knowledge retrieval (RAG) app with the Azure AI Foundry SDK
 
-In this tutorial, you use the Azure AI Foundry SDK (and other libraries) to build, configure, evaluate, and deploy a chat app for your retail company called Contoso Trek. Your retail company specializes in outdoor camping gear and clothing. The chat app should answer questions about your products and services. For example, the chat app can answer questions such as "which tent is the most waterproof?" or "what is the best sleeping bag for cold weather?".
+In this tutorial, you use the Azure AI Foundry SDK (and other libraries) to build, configure, and evaluate a chat app for your retail company called Contoso Trek. Your retail company specializes in outdoor camping gear and clothing. The chat app should answer questions about your products and services. For example, the chat app can answer questions such as "which tent is the most waterproof?" or "what is the best sleeping bag for cold weather?".
 
 This part two shows you how to enhance a basic chat application by adding [retrieval augmented generation (RAG)](../concepts/retrieval-augmented-generation.md) to ground the responses in your custom data. Retrieval Augmented Generation (RAG) is a pattern that uses your data with a large language model (LLM) to generate answers specific to your data. In this part two, you learn how to:
 
 > [!div class="checklist"]
+> - Get example data
 > - Create a search index of the data for the chat app to use
 > - Develop custom RAG code