fixing wording

Author: fosteramanda

articles/ai-services/agents/includes/file-search/azure-blob-storage-code-examples.md

@@ -1,5 +1,5 @@
 ## Quickstart – Use existing files in Azure Blob Storage with file search
-In this example, we’ll use Azure AI Agent Service to create an agent that can help answer questions on information from files in Azure Blob Storage.
+In this example, we use Azure AI Agent Service to create an agent that can help answer questions on information from files in Azure Blob Storage.
 
 ###  Prerequisites 
 Complete the [standard agent setup](../../quickstart.md).
@@ -25,10 +25,10 @@ project_client = AIProjectClient.from_connection_string(
 )
 ```
 ### Step 2: Upload local files to your project Azure Blob Storage container
-We will upload the local file to your project Azure Blob Storage container. This is the same storage account you connected to your agent during the agent setup. 
-The project asset ID is the URI of the uploaded file and we print out this value. If you create more agents in the same project that want to use the uploaded file, you can reuse this asset ID. That way you don't need to upload the file again.
+We upload the local file to your project Azure Blob Storage container. This is the same storage account you connected to your agent during the agent setup. 
+The project asset ID is the URI of the uploaded file and we print this value. If you create more agents in the same project that want to use the uploaded file, you can reuse this asset ID. That way you don't need to upload the file again.
 ```python
-# We will upload the local file to your project Azure Blob Storage container and will use it for vector store creation.
+# We'll upload the local file to your project Azure Blob Storage container and will use it for vector store creation.
 _, asset_uri = project_client.upload_file("C:\\Users\\fosteramanda\\Downloads\\hub bicep\\azure-ai-agents\\data\\product_info_1.md")
 print(f"Uploaded file, asset URI: {asset_uri}")
 
@@ -75,7 +75,7 @@ print(f"Messages: {messages}")
 ```
 
 ### Step 4: Create second vector store using the previously uploaded file
-Now we will create a second vector store using the previously uploaded file. This is useful if you have multiple agents that need access to the same files.
+Now we create a second vector store using the previously uploaded file. Using the asset_uri of file already in Azure Blob Storage is useful if you have multiple agents that need access to the same files. That way you don't need to upload the same file multiple times.
 ```python
 
 # create a vector store with a previously uploaded file and wait for it to be processed

articles/ai-services/agents/includes/file-search/upload-files-code-examples.md

@@ -1,13 +1,13 @@
 ## Quickstart – Upload Local Files with file search 
 
-In this example, we’ll use Azure AI Agent Service to create an agent that can help answer questions on information you upload from local files.  
+In this example, we use Azure AI Agent Service to create an agent that can help answer questions on information you upload from local files.  
 
 ##  Prerequisites 
 Complete the [agent setup](../../quickstart.md).
 
 ## Step 1: Create a project client
 
-Create a client object, which will contain the connection string for connecting to your AI project and other resources. 
+Create a client object, that contains the connection string for connecting to your AI project and other resources. 
 
 # [Python](#tab/python)