fixes

docs/ai/quickstarts/get-started-openai.md

@@ -32,7 +32,7 @@ Get started with Semantic Kernel by creating a simple .NET 8 console chat applic
 
 :::zone-end
 
-## Trying HikerAI sample
+## Try the HikerAI sample
 
 <!-- markdownlint-disable MD029 MD044 -->
 :::zone target="docs" pivot="openai"
@@ -69,9 +69,9 @@ Get started with Semantic Kernel by creating a simple .NET 8 console chat applic
 
 ## Explore the code
 
-The application uses the [`Microsoft.SemanticKernel`](https://www.nuget.org/packages/Microsoft.SemanticKernel) package to send and receive requests to the OpenAI service.
+The app uses the [`Microsoft.SemanticKernel`](https://www.nuget.org/packages/Microsoft.SemanticKernel) package to send and receive requests to the OpenAI service.
 
-The entire application is contained within the **Program.cs** file. The first several lines of code set configuration values and gets the OpenAI Key that was previously set using the `dotnet user-secrets` command.
+The app code is contained within the **Program.cs** file. The first several lines of code set configuration values and gets the OpenAI Key that was previously set using the `dotnet user-secrets` command.
 
 ```csharp
 var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
@@ -145,7 +145,8 @@ chatHistory.AddUserMessage("Hi! Apparently you can help me find a hike that I wi
 Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
 
 // Get response
-var response = await service.GetChatMessageContentAsync(chatHistory, new OpenAIPromptExecutionSettings() { MaxTokens = 400 });
+var response = await service.GetChatMessageContentAsync(
+    chatHistory, new OpenAIPromptExecutionSettings() { MaxTokens = 400 });
 ```
 
 Add the response from the mode to maintain the chat history.

docs/ai/quickstarts/quickstart-ai-chat-with-data.md

@@ -30,7 +30,7 @@ Get started with AI development, using the `gpt-35-turbo` model from a simple .N
 [!INCLUDE [download-alert](includes/prerequisites-azure-openai.md)]
 :::zone-end
 
-## Try "Chatting About My Previous Hikes" sample
+## Try the "Chatting About My Previous Hikes" sample
 
 <!-- markdownlint-disable MD029 MD044 -->
 :::zone target="docs" pivot="openai"
@@ -128,18 +128,24 @@ To have the model generate a response based off the system prompt and the user r
 chatHistory.AddUserMessage("Hi!");
 Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
 
-chatHistory.Add(await service.GetChatMessageContentAsync(chatHistory, new OpenAIPromptExecutionSettings() { MaxTokens = 400 }));
+chatHistory.Add(
+    await service.GetChatMessageContentAsync(
+        chatHistory,
+        new OpenAIPromptExecutionSettings() { MaxTokens = 400 }));
 Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
 ```
 
 To maintain the chat history or context, make sure you add the response from the model to the `chatHistory`. It's time to make our user request about our data again using the `AddUserMessage` and `GetChatMessageContentAsync` function.
 
 ```csharp
 // Continue the conversation with a question.
-chatHistory.AddUserMessage("I would like to know the ratio of the hikes I've done in Canada compared to other countries.");
+chatHistory.AddUserMessage(
+    "I would like to know the ratio of the hikesI've done in Canada compared to other countries.");
 Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
 
-chatHistory.Add(await service.GetChatMessageContentAsync(chatHistory, new OpenAIPromptExecutionSettings() { MaxTokens = 400 }));
+chatHistory.Add(await service.GetChatMessageContentAsync(
+    chatHistory,
+    new OpenAIPromptExecutionSettings() { MaxTokens = 400 }));
 Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
 ```
 

docs/ai/quickstarts/quickstart-azure-openai-tool.md

@@ -32,7 +32,7 @@ Get started with AI by creating a simple .NET 8 console chat application. The ap
 
 :::zone-end
 
-## Try HikerAI Pro sample
+## Try the the HikerAI Pro sample
 
 <!-- markdownlint-disable MD029 MD044 -->
 :::zone target="docs" pivot="openai"
@@ -124,7 +124,8 @@ The function's `ImportPluginFromFunctions` and `CreateFromMethod` are used to de
 // For convenience and clarity of into the code, this standalone local method handles tool call responses. It will fake a call to a weather API and return the current weather for the specified location.
 kernel.ImportPluginFromFunctions("WeatherPlugin",
 [
-    KernelFunctionFactory.CreateFromMethod(([Description("The city, e.g. Montreal, Sidney")] string location, string unit = null) =>
+    KernelFunctionFactory.CreateFromMethod(
+        ([Description("The city, e.g. Montreal, Sidney")] string location, string unit = null) =>
     {
         // Here you would call a weather API to get the weather for the location
         return "Periods of rain or drizzle, 15 C";
@@ -136,16 +137,18 @@ Once the `kernel` client is created, provide more context to the model by adding
 
 ```csharp
 ChatHistory chatHistory = new("""
-    You are a hiking enthusiast who helps people discover fun hikes in their area. You are upbeat and friendly.
-    A good weather is important for a good hike. Only make recommendations if the weather is good or if people insist.
-    You introduce yourself when first saying hello. When helping people out, you always ask them 
-    for this information to inform the hiking recommendation you provide:
+    You are a hiking enthusiast who helps people discover fun hikes in their area.
+    You are upbeat and friendly. A good weather is important for a good hike. 
+    Only make recommendations if the weather is good or if people insist.
+    You introduce yourself when first saying hello. When helping people out,
+    you always ask them for this information to inform the hiking recommendation you provide:
 
     1. Where they are located
     2. What hiking intensity they are looking for
 
-    You will then provide three suggestions for nearby hikes that vary in length after you get that information. 
-    You will also share an interesting fact about the local nature on the hikes when making a recommendation.
+    You will then provide three suggestions for nearby hikes that vary in length
+    after you get that information. You will also share an interesting fact about the local
+    nature on the hikes when making a recommendation.
     """);
 ```
 
@@ -156,8 +159,9 @@ To have the model generate a response based off the system prompt and the user r
 ```csharp
 chatHistory.AddUserMessage("""
     Is the weather is good today for a hike?
-    If yes, I live in the greater Montreal area and would like an easy hike. I don't mind driving a bit to get there.
-    I don't want the hike to be over 10 miles round trip. I'd consider a point-to-point hike.
+    If yes, I live in the greater Montreal area and would like an easy hike. 
+    I don't mind driving a bit to get there. I don't want the hike to be over 10 miles round trip.
+    I'd consider a point-to-point hike.
     I want the hike to be as isolated as possible. I don't want to see many people.
     I would like it to be as bug free as possible.
     """);

docs/ai/quickstarts/quickstart-openai-generate-images.md

@@ -32,7 +32,7 @@ Get started with AI by creating a simple .NET 8 console chat application. The ap
 
 :::zone-end
 
-## Trying the Hiking Images sample
+## Try the the Hiking Images sample
 
 <!-- markdownlint-disable MD029 MD044 -->
 :::zone target="docs" pivot="openai"

docs/ai/quickstarts/quickstart-openai-summarize-text.md

@@ -16,7 +16,7 @@ zone_pivot_groups: openai-library
 :::zone target="docs" pivot="openai"
 <!-- markdownlint-enable MD044 -->
 
-Get started with AI by creating a simple .NET 8 console chat application to summarize text. The application will run locally and use the OpenAI `gpt-3.5-turbo` model. Follow these steps to get access to OpenAI and learn how to use Semantic Kernel.
+Get started with AI by creating a simple .NET 8 console chat application to summarize text. The application runs locally and uses the OpenAI `gpt-3.5-turbo` model. Follow these steps to get access to OpenAI and learn how to use Semantic Kernel.
 
 [!INCLUDE [download-alert](includes/prerequisites-openai.md)]
 
@@ -26,13 +26,13 @@ Get started with AI by creating a simple .NET 8 console chat application to summ
 :::zone target="docs" pivot="azure-openai"
 <!-- markdownlint-enable MD044 -->
 
-Get started with AI by creating a simple .NET 8 console chat application to summarize text. The application will run locally and connect to the OpenAI `gpt-35-turbo` model deployed into Azure OpenAI. Follow these steps to provision Azure OpenAI and learn how to use Semantic Kernel.
+Get started with AI by creating a simple .NET 8 console chat application to summarize text. The app runs locally and connects to the OpenAI `gpt-35-turbo` model deployed into Azure OpenAI. Follow these steps to provision the Azure OpenAI service and learn how to use Semantic Kernel.
 
 [!INCLUDE [download-alert](includes/prerequisites-azure-openai.md)]
 
 :::zone-end
 
-## Trying Hiking Benefits Summary sample
+## Try the Hiking Benefits Summary sample
 
 <!-- markdownlint-disable MD029 MD044 -->
 :::zone target="docs" pivot="openai"
@@ -91,7 +91,7 @@ Kernel kernel = Kernel.CreateBuilder()
 :::zone target="docs" pivot="azure-openai"
 The application uses the [`Microsoft.SemanticKernel`](https://www.nuget.org/packages/Microsoft.SemanticKernel) package to send and receive requests to the Azure OpenAI service.
 
-The entire application is contained within the **Program.cs** file. The first several lines of code loads up secrets and configuration values that were set in the `dotnet user-secrets` for you during the application provisioning.
+The entire application is contained within the **Program.cs** file. The first several lines of code retrieve secrets and configuration values that were set in the `dotnet user-secrets` for you during the application provisioning.
 
 ```csharp
 // == Retrieve the local secrets saved during the Azure deployment ==========
@@ -123,7 +123,7 @@ string prompt = $"""
 Console.WriteLine($"user >>> {prompt}");
 ```
 
-To have the model generate a response based off `prompt`, use the `InvokePromptAsync` function.
+Use the `InvokePromptAsync` function to have the model generate a response based on the `prompt` value.
 
 ```csharp
 // Submit the prompt and print out the response