update create project

laujan · laujan · commit b6fffb6e17c3 · 2025-07-24T13:53:10.000-07:00
diff --git a/articles/ai-services/language-service/conversational-language-understanding/concepts/data-formats.md b/articles/ai-services/language-service/conversational-language-understanding/concepts/data-formats.md
@@ -17,7 +17,7 @@ If you're uploading your data into conversational language understanding, it mus
 
 ## Import project file format
 
-If you're [importing a project](../how-to/create-project.md#import-a-project) into conversational language understanding, the file uploaded must be in the following format:
+If you're [importing a project](../how-to/create-project.md#import-an-existing-azure-ai-project) into conversational language understanding, the file uploaded must be in the following format:
 
 ```json
 {
@@ -101,7 +101,7 @@ If you're [importing a project](../how-to/create-project.md#import-a-project) in
 |Key  |Placeholder  |Value  | Example |
 |---------|---------|----------|--|
 |`{API-VERSION}`     | The [version](../../concepts/model-lifecycle.md#api-versions) of the API you're calling. | `2023-04-01` |
-|`confidenceThreshold`|`{CONFIDENCE-THRESHOLD}`|This is the threshold score below which the intent is predicted as [None intent](none-intent.md). Values are from `0` to `1`.|`0.7`|
+|`confidenceThreshold`|`{CONFIDENCE-THRESHOLD}`|The threshold score for which the intent is predicted as [None intent](none-intent.md). Values are from `0` to `1`.|`0.7`|
 | `projectName` | `{PROJECT-NAME}` | The name of your project. This value is case sensitive. | `EmailApp` |
 | `multilingual` | `true`| A Boolean value that enables you to have utterances in multiple languages in your dataset. When your model is deployed, you can query the model in any supported language (not necessarily included in your training documents. For more information about supported language codes, see [Language support](../language-support.md#multi-lingual-option). | `true`|
 |`sublists`|`[]`|Array that contains sublists. Each sublist is a key and its associated values.|`[]`|
@@ -110,7 +110,7 @@ If you're [importing a project](../how-to/create-project.md#import-a-project) in
 | `language` | `{LANGUAGE-CODE}` |  A string specifying the language code for the utterances, synonyms, and regular expressions used in your project. If your project is a multilingual project, choose the [language code](../language-support.md) of most the utterances. |`en-us`|
 | `intents` | `[]` | Array that contains all the intents you have in the project. These intents are classified from your utterances.| `[]` |
 | `entities` | `[]` | Array that contains all the entities in your project. These entities are extracted from your utterances. Every entity can have other optional components defined with them: list, prebuilt, or regex. | `[]` |
-| `dataset` | `{DATASET}` |  The test set to which this utterance goes to when it's split before training. To learn more about data splitting, see [Train your conversational language understanding model](../how-to/train-model.md#data-splitting). Possible values for this field are `Train` and `Test`.      |`Train`|
+| `dataset` | `{DATASET}` |  The test set that this utterance is assigned to when the data is split before training. To learn more about data splitting, see [Train your conversational language understanding model](../how-to/train-model.md#data-splitting). Possible values for this field are `Train` and `Test`.      |`Train`|
 | `category` | ` ` |  The type of entity associated with the span of text specified. | `Entity1`|
 | `offset` | ` ` |  The inclusive character position of the start of the entity.      |`5`|
 | `length` | ` ` |  The character length of the entity.      |`5`|
@@ -166,7 +166,7 @@ Conversational language understanding offers the option to upload your utterance
 |---------|---------|----------|--|
 |`text`|`{Utterance-Text}`|Your utterance text.|Testing|
 | `language` | `{LANGUAGE-CODE}` |  A string that specifies the language code for the utterances used in your project. If your project is a multilingual project, choose the language code of most of the utterances. For more information about supported language codes, see [Language support](../language-support.md). |`en-us`|
-| `dataset` | `{DATASET}` |  The test set to which this utterance goes to when it's split before training. To learn more about data splitting, see [Train your conversational language understanding model](../how-to/train-model.md#data-splitting). Possible values for this field are `Train` and `Test`.      |`Train`|
+| `dataset` | `{DATASET}` |  The test set that this utterance is assigned to when the data is split before training. To learn more about data splitting, see [Train your conversational language understanding model](../how-to/train-model.md#data-splitting). Possible values for this field are `Train` and `Test`.      |`Train`|
 |`intent`|`{intent}`|The assigned intent.| intent1|
 |`entity`|`{entity}`|The entity to be extracted.| entity1|
 | `category` | ` ` |  The type of entity associated with the span of text specified. | `Entity1`|
@@ -175,5 +175,5 @@ Conversational language understanding offers the option to upload your utterance
 
 ## Related content
 
-* For more information on importing your labeled data into your project directly, see [Import project](../how-to/create-project.md#import-a-project).
+* For more information on importing your labeled data into your project directly, see [Import project](../how-to/create-project.md#import-an-existing-azure-ai-project).
 * For more information about labeling your data, see [Label your utterances in Language Studio](../how-to/tag-utterances.md). After you label your data, you can [train your model](../how-to/train-model.md).
diff --git a/articles/ai-services/language-service/conversational-language-understanding/how-to/create-project.md b/articles/ai-services/language-service/conversational-language-understanding/how-to/create-project.md
@@ -93,7 +93,8 @@ You can export a CLU project as a JSON file at any time.
 
 ### [Azure AI Foundry](#tab/azure-ai-foundry)
 
-1. In [Azure AI Foundry portal](https://ai.azure.com), open your [!INCLUDE [hub-project-name](../includes/hub-project-name.md)]. 
+
+If you no longer need your project, you can delete it from the Azure AI Foundry.
 
 1. Navigate to the [Azure AI Foundry](https://ai.azure.com/) home page and sign in if you haven't already done so.
 1. Select the project that you want to delete from the **Keep building with Azure AI Foundry**
@@ -108,7 +109,7 @@ To delete the hub along with all its projects:
 
 ### [REST APIs](#tab/rest-api)
 
-When you don't need your project anymore, you can use the APIs to delete your project.
+If you no longer need your project, you can delete it using the REST API.
 
 [!INCLUDE [Delete project](../includes/rest-api/delete-project.md)]
 
diff --git a/articles/ai-services/language-service/conversational-language-understanding/how-to/tag-utterances.md b/articles/ai-services/language-service/conversational-language-understanding/how-to/tag-utterances.md
@@ -15,7 +15,7 @@ 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-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.
+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-an-existing-azure-ai-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-an-existing-azure-ai-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.
@@ -43,9 +43,9 @@ As you add utterances and label them, keep in mind:
 
 * For [multilingual projects](../language-support.md#multi-lingual-option), adding utterances in other languages increases the model's performance in these languages. Avoid duplicating your data across all the languages that you want to support. For example, to improve a calender bot's performance with users, a developer might add examples mostly in English and a few in Spanish or French. They might add utterances such as:
 
-  * "Set a meeting with **Matt** and **Kevin** **tomorrow** at **12 PM**." (English)
-  * "Reply as **tentative** to the **weekly update** meeting." (English)
-  * "Cancelar mi **próxima** reunión." (Spanish)
+  * `Set a meeting with **Matt** and **Kevin** **tomorrow** at **12 PM**.` (English)
+  * `Reply as **tentative** to the **weekly update** meeting.` (English)
+  * `Cancelar mi **próxima** reunión.` (Spanish)
 
 ## Label your utterances
 
@@ -111,7 +111,7 @@ Before you get started, the suggested utterances feature is available only if yo
 
 On the **Data labeling** page:
 
-1. Select **Suggest utterances**. A pane opens on the right and prompts you to select your Azure OpenAI resource and deployment.
+1. Select **Suggest utterances**. A pane opens and prompts you to select your Azure OpenAI resource and deployment.
 1. After you select an Azure OpenAI resource, select **Connect** so that your Language resource has direct access to your Azure OpenAI resource. It assigns your Language resource the **Cognitive Services User** role to your Azure OpenAI resource. Now your current Language resource has access to Azure OpenAI. If the connection fails, follow [these steps](#add-required-configurations-to-azure-openai-resource) to manually add the correct role to your Azure OpenAI resource.
 1. After the resource is connected, select the deployment. The model that we recommend for the Azure OpenAI deployment is `gpt-35-turbo-instruct`.
 1. Select the intent for which you want to get suggestions. Make sure the intent that you selected has at least five saved utterances to be enabled for utterance suggestions. The suggestions provided by Azure OpenAI are based on the most recent utterances that you added for that intent.
diff --git a/articles/ai-services/language-service/toc.yml b/articles/ai-services/language-service/toc.yml
@@ -160,7 +160,7 @@ items:
         - name: Train a model
           href: conversational-language-understanding/how-to/train-model.md
           displayName: training, clu training, conversational model training
-        - name: Configure Azure resources
+        - name: Configure Azure and CLU resources
           href: conversational-language-understanding/how-to/configure-azure-resources.md
           displayName: configuration, fine-tuning, azure ai foundry, azure portal 
         - name: View your model's performance
@@ -564,7 +564,7 @@ items:
         href: question-answering/how-to/chit-chat.md
       - name: Change default answer
         href: question-answering/how-to/change-default-answer.md
-      - name: Configure Azure resources
+      - name: Configure your environment and Azure resources
         href: question-answering/how-to/configure-azure-resources.md
         displayName: configuration, fine-tuning, azure ai foundry, azure portal 
       - name: Analytics
