wording tweaks

Author: PatrickFarley

articles/cognitive-services/personalizer/includes/quickstart-sdk-csharp.md

@@ -68,15 +68,15 @@ dotnet add package Microsoft.Azure.CognitiveServices.Personalizer --version 1.0.
 
 ## Code block 1: Generate sample data
 
-Personalizer is meant to run on applications that receive and interpret real-time data. For the purpose of this quickstart, you'll use sample code to generate imaginary customer actions on a grocery website. The following code block defines three key methods: **GetActions**, **GetContext** and **GetRewardScore**.
+Personalizer is meant to run on applications that receive and interpret real-time data. In this quickstart, you'll use sample code to generate imaginary customer actions on a grocery website. The following code block defines three key methods: **GetActions**, **GetContext** and **GetRewardScore**.
 
 - **GetActions** returns a list of the choices that the grocery website needs to rank. In this example, the actions are meal products. Each action choice has details (features) that may affect user behavior later on. Actions are used as input for the Rank API
 
 - **GetContext** returns a simulated customer visit. It selects randomized details (context features) like which customer is present and what time of day the visit is taking place. In general, a context represents the current state of your application, system, environment, or user. The context object is used as input for the Rank API.
 
-   The context features in this quickstart are simplistic. However, in a real production system, designing your [features](../concepts-features.md) and [evaluating their effectiveness](../how-to-feature-evaluation.md) is very important. Refer to the linked documentation for guidance.
+   The context features in this quickstart are simplistic. However, in a real production system, designing your [features](../concepts-features.md) and [evaluating their effectiveness](../how-to-feature-evaluation.md) is important. Refer to the linked documentation for guidance.
 
-- **GetRewardScore** returns a score between zero and one that represents the success of a customer interaction. It uses simple logic to determine how different contexts will respond to different action choices. For example, a certain user will always give a 1.0 for vegetarian and vegan products, and a 0.0 for other products. In a real scenario, Personalizer will learn user preferences from the data sent in Rank and Reward API calls. You won't define these explicitly as in the example code.
+- **GetRewardScore** returns a score between zero and one that represents the success of a customer interaction. It uses simple logic to determine how different contexts respond to different action choices. For example, a certain user will always give a 1.0 for vegetarian and vegan products, and a 0.0 for other products. In a real scenario, Personalizer will learn user preferences from the data sent in Rank and Reward API calls. You won't define these explicitly as in the example code.
 
     In a real production system, the [reward score](../concept-rewards.md) should be designed to align with your business objectives and KPIs. Determining how to calculate the reward metric may require some experimentation.
 
@@ -97,7 +97,7 @@ Personalizer is meant to run on applications that receive and interpret real-tim
 
 ## Code block 2: Iterate the learning loop
 
-The next block of code defines the **main** method and closes out the script. It runs a learning loop iteration, in which it generates a context (including a customer), requests a ranking for actions in that context using the Rank API, calculates the reward score, and passes that score back to the Personalizer service using the Reward API. It prints relevant information to the console at each step.
+The next block of code defines the **main** method and closes out the script. It runs a learning loop iteration, in which it generates a context (including a customer), requests a ranking of actions in that context using the Rank API, calculates the reward score, and passes that score back to the Personalizer service using the Reward API. It prints relevant information to the console at each step.
 
 In this example, each Rank call is made to determine which product should be displayed in the "Featured Product" section. Then the Reward call indicates whether or not the featured product was purchased by the user. Rewards are associated with their decisions through a common `EventId` value.