Edits for readability

Author: HeidiSteen

articles/search/search-get-started-rag.md

@@ -31,8 +31,7 @@ This quickstart shows you how to send queries to a chat completion model for a c
   - Same region as Azure OpenAI.
   - Basic tier or higher is recommended.
   - [Enable semantic ranking](semantic-how-to-enable-disable.md).
-  - Enable role-based access control.
-  - Enable a system identity for Azure AI Search.
+  - [Enable role-based access control (see below)](#configure-access).
 
 To meet the same-region requirement, start by reviewing the [regions for the chat model](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability) you want to use. Once you identify a region, confirm that Azure AI Search is available in the [same region](search-region-support.md#azure-public-regions).
 
@@ -79,7 +78,7 @@ It can take several minutes for permissions to take effect.
 
 ## Create an index
 
-We recommend the hotels-sample-index, which can be created in minutes and runs on any search service tier. This index is created using built-in sample data.
+A search index provides grounding data for the chat model. We recommend the hotels-sample-index, which can be created in minutes and runs on any search service tier. This index is created using built-in sample data.
 
 1. In the Azure portal, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
 

articles/search/tutorial-rag-build-solution-index-schema.md

@@ -106,7 +106,7 @@ A minimal index for LLM is designed to store chunks of content. It typically inc
 
    Like the basic schema, it's organized around chunks. The `chunk_id` uniquely identifies each chunk. The `text_vector` field is an embedding of the chunk. The nonvector `chunk` field is a readable string. The `title` maps to a unique metadata storage path for the blobs. The `parent_id` is the only parent-level field, and it's a base64-encoded version of the parent file URI. 
 
-   In integrated vectorization workloads, the `dimensions` property on your vector fields should be identical to the number of `dimensions` generated by the embedding skill you're using to vectorize your data. In this tutorial, the embedding skill is the Azure OpenAI embedding skill that calls the text-embedding-3-large model. The skill is specified in the next tutorial.
+   In integrated vectorization workloads like the one used in this tutorial series, the `dimensions` property on your vector fields should be identical to the number of `dimensions` generated by the embedding skill used to vectorize your data. In this series, we use the Azure OpenAI embedding skill, which calls the text-embedding-3-large model on Azure OpenAI. The skill is specified in the next tutorial. We set dimensions to 1024 in both the vector field and in the skill definition.
 
    The schema also includes a `locations` field for storing generated content that's created by the [indexing pipeline](tutorial-rag-build-solution-pipeline.md).
 

articles/search/vector-search-how-to-create-index.md

@@ -281,7 +281,7 @@ Vector fields are characterized by [their data type](/rest/api/searchservice/sup
 1. Define a vector field with the following attributes. You can store one generated embedding per field. For each vector field:
 
    + `type` must be a [vector data types](/rest/api/searchservice/supported-data-types#edm-data-types-for-vector-fields). `Collection(Edm.Single)` is the most common for embedding models.
-   + `dimensions` is the number of dimensions generated by the embedding model. For text-embedding-ada-002, it's fixed at 1536. For text-embedding-3 model series, there's a range of values. If you're using integrated vectorization and an embedding skill to generate vectors, make sure this property is set to the [same dimensions value](cognitive-search-skill-azure-openai-embedding.md#supported-dimensions-by-modelname) used by the embedding skill.
+   + `dimensions` is the number of dimensions generated by the embedding model. For text-embedding-ada-002, it's fixed at 1536. For the text-embedding-3 model series, there's a range of values. If you're using integrated vectorization and an embedding skill to generate vectors, make sure this property is set to the [same dimensions value](cognitive-search-skill-azure-openai-embedding.md#supported-dimensions-by-modelname) used by the embedding skill.
    + `vectorSearchProfile` is the name of a profile defined elsewhere in the index.
    + `searchable` must be true.
    + `retrievable` can be true or false. True returns the raw vectors (1536 of them) as plain text and consumes storage space. Set to true if you're passing a vector result to a downstream app.