Merge branch 'how-to-guides-for-clu' of https://github.com/paulth1/azure-ai-docs-pr into how-to-guides-for-clu

Author: paulth1

articles/ai-services/language-service/conversational-language-understanding/how-to/build-schema.md

@@ -86,7 +86,7 @@ To add a regex component, select **Add expression**. Name the regex key, and ent
 
 ### Define entity options
 
-Select the **Entity Options** tab on the entity details page. When multiple components are defined for an entity, their predictions might overlap. When an overlap occurs, each entity's final prediction is determined based on the [entity option](../concepts/entity-components.md#entity-options) that you select in this step. Select the option that you want to apply to this entity and select **Save**.
+Select the **Entity Options** tab on the entity details page. When multiple components are defined for an entity, their predictions might overlap. When an overlap occurs, each entity's final prediction is determined based on the [entity option](../concepts/entity-components.md#entity-options) that you select in this step. Select the option that you want to apply to this entity, and then select **Save**.
 
    <!--:::image type="content" source="../media/entity-options.png" alt-text="A screenshot that shows an entity option in Language Studio." lightbox="../media/entity-options.png":::-->
 

articles/ai-services/language-service/conversational-language-understanding/how-to/create-project.md

@@ -49,7 +49,7 @@ After you create a Language resource, create a CLU project.
 
 ---
 
-## Import project
+## Import a project
 
 ### [Azure AI Foundry](#tab/azure-ai-foundry)
 
@@ -73,11 +73,11 @@ You can import a CLU JSON into the service.
 
 ---
 
-## Export project
+## Export a project
 
 ### [Azure AI Foundry](#tab/azure-ai-foundry)
 
-You can export a CLU project as a JSON file at any time. On the conversation projects page, select a project, and select **Export**.
+You can export a CLU project as a JSON file at any time. On the conversation projects page, select a project, and then select **Export**.
 
 ### [REST APIs](#tab/rest-api)
 
@@ -99,7 +99,7 @@ You can export a CLU project as a JSON file at any time.
 
 ---
 
-## Delete project
+## Delete a project
 
 ### [Azure AI Foundry](#tab/azure-ai-foundry)
 

articles/ai-services/language-service/conversational-language-understanding/how-to/tag-utterances.md

@@ -15,10 +15,10 @@ ms.custom: language-service-clu
 
 After you [build a schema](build-schema.md) for your fine-tuning task, you add training utterances to your project. The utterances should be similar to what your users use when they interact with the project. When you add an utterance, you have to assign which intent it belongs to. After the utterance is added, label the words within your utterance that you want to extract as entities.
 
-Data labeling is a crucial step in the conversational language understanding (CLU) trained development lifecycle. This data is used in the next step when you train your model so that your model can learn from the labeled data. If you already labeled utterances, you can directly [import them into your project](create-project.md#import-project), if your data follows the [accepted data format](../concepts/data-formats.md). To learn more about importing labeled data, see [Create a CLU fine-tuning task](create-project.md#import-project). Labeled data informs the model about how to interpret text and is used for training and evaluation.
+Data labeling is a crucial step in the conversational language understanding (CLU) trained development lifecycle. This data is used in the next step when you train your model so that your model can learn from the labeled data. If you already labeled utterances, you can directly [import them into your project](create-project.md#import-a-project), if your data follows the [accepted data format](../concepts/data-formats.md). To learn more about importing labeled data, see [Create a CLU fine-tuning task](create-project.md#import-a-project). Labeled data informs the model about how to interpret text and is used for training and evaluation.
 
-   > [!TIP]
-  > Use the **Quick Deploy** option to implement custom CLU intent routing, which is powered by your own large language model deployment without adding or labeling any training data.
+> [!TIP]
+> Use the **Quick Deploy** option to implement custom CLU intent routing, which is powered by your own large language model deployment without adding or labeling any training data.
 
 ## Prerequisites
 

articles/ai-services/language-service/conversational-language-understanding/includes/language-studio/create-project.md

@@ -24,4 +24,4 @@ ms.author: lajanuar
     |Utterances primary language     | The primary language of your project. Your training data should primarily be in this language.        |
     |Enable multiple languages    |  Whether you want to enable your project to support [multiple languages](../../language-support.md#multi-lingual-option) at once.       |
 
-1. After you're finished, select **Create**. You should now see the **Getting started** landing page for your project.
+1. After you finish, select **Create**. You should now see the **Getting started** landing page for your project.