Incorporated Matt's feedback on choose a model

Author: HeidiSteen

articles/search/tutorial-rag-build-solution-models.md

@@ -36,7 +36,7 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 
 - A model provider, such as [Azure OpenAI](/azure/ai-services/openai/how-to/create-resource), Azure AI Vision via an [Azure AI multi-service account](/azure/ai-services/multi-service-resource), or [Azure AI Studio](https://ai.azure.com/).
 
-  We use Azure OpenAI in this tutorial. Other providers are listed so that you know your options for integrated embedding.
+  We use Azure OpenAI in this tutorial. Other providers are listed so that you know your options for integrated vectorization.
 
 - Azure AI Search, Basic tier or higher provides a [managed identity](search-howto-managed-identities-data-sources.md) used in role assignments. 
 
@@ -48,16 +48,18 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 
   - [Azure AI Studio](/azure/ai-studio/reference/region-support) regions. 
 
-  Azure AI Search is currently facing limited availability in some regions, such as West Europe and West US 2/3. Check the [Azure AI Search region list](search-region-support.md) to confirm availability.
+  Azure AI Search is currently facing limited availability in some regions, such as West Europe and West US 2/3. Check the [Azure AI Search region list](search-region-support.md) to confirm region status.
 
 > [!TIP]
-> Currently, the following regions provide the most overlap and have the most capacity: **East US**, **East US2**, and **South Central** in the Americas; **France Central** or **Switzerland North** in Europe; **Australia East** in Asia Pacific.
+> Currently, the following regions provide the most overlap among the model providers and have the most capacity: **East US**, **East US2**, and **South Central** in the Americas; **France Central** or **Switzerland North** in Europe; **Australia East** in Asia Pacific.
+>
+> For Azure AI Vision and AI Search interoperability, choose one of these regions: **East US**, **France Central**, **Korea Central**, **North Europe**, **South East Asia**, or **West US**. 
 
 ## Review models supporting built-in vectorization
 
-Azure AI Search supports an embedding action in an indexing pipeline. It also supports an embedding action at query time, converting text or image inputs into vectors for a vector search. In this step, identify an embedding model that works for your content and queries. If you're providing raw vector data and raw vector queries, or if your RAG solution doesn't include vector data, skip this step.
+Vectorized content improves the query results in a RAG solution. Azure AI Search supports an embedding action in an indexing pipeline. It also supports an embedding action at query time, converting text or image inputs into vectors for a vector search. In this step, identify an embedding model that works for your content and queries. If you're providing raw vector data and raw vector queries, or if your RAG solution doesn't include vector data, skip this step.
 
-Vector queries work best when you use the same embedding model for both indexing and query input conversions. Using different embedding models for each action typically results in poor query outcomes.
+Vector queries that include a text-to-vector conversion step must use the same embedding model that was used during indexing. The search engine won't throw an error if you use different models, but you'll get poor results.
 
 To meet the same-model requirement, choose embedding models that can be referenced through *skills* during indexing and through *vectorizers* during query execution. Review [Create an indexing pipeline](tutorial-rag-build-solution-pipeline.md) for code that calls an embedding skill and a matching vectorizer.