Merge pull request #96634 from diberry/diberry/1120-hack-tutorial-intents

[Cogsvcs] HACK - tutorial intents

Author: Jak-MS

articles/cognitive-services/LUIS/includes/add-example-utterances-none-intent.md

@@ -8,22 +8,24 @@ ms.service: cognitive-services
 ms.subservice: luis
 ms.topic: include
 ms.custom: include file
-ms.date: 11/05/2019
+ms.date: 11/20/2019
 ms.author: diberry
 ---
 
-The client application needs to know if an utterance is not meaningful or appropriate for the application. The **None** intent is added to each application as part of the creation process to determine if an utterance can't be answered by the client application.
+The client application needs to know if an utterance is not meaningful or appropriate for the application. The **None** intent is added to each application as part of the creation process to determine if an utterance shouldn't be answered by the client application.
 
 If LUIS returns the **None** intent for an utterance, your client application can ask if the user wants to end the conversation or give more directions for continuing the conversation. 
 
 If you leave the **None** intent empty, an utterance that should be predicted outside the subject domain will be predicted in one of the existing subject domain intents. The result is that the client application, such as a chat bot, will perform incorrect operations based on an incorrect prediction. 
 
 1. Select **Intents** from the left panel.
 
-1. Select the **None** intent. Add three utterances that your user might enter but are not relevant to your app:
+1. Select the **None** intent. Add three utterances that your user might enter but are not relevant to your Pizza ordering app:
 
     |`None` example utterances|
     |--|
     |`Barking dogs are annoying`|
     |`Order a pizza for me`|
     |`Penguins in the ocean`|
+
+    These examples shouldn't use words you expect in your subject domain such as `pizza`, `cheese`, `crust`, `pickup` `deliver`.

articles/cognitive-services/LUIS/includes/howto-publish.md

@@ -23,7 +23,7 @@ In order to receive a LUIS prediction in a chat bot or other client application,
 
     ![LUIS publish to endpoint](../media/howto-publish/publish-app-popup.png)
 
-3. Publishing is complete when you see the green status bar at the top of the website confirming success.
+3. Publishing is complete when you see the status bar at the top of the page confirming success.
 
     ![LUIS publish to endpoint](../media/howto-publish/publish-endpoint-success.png)
 

articles/cognitive-services/LUIS/tutorial-intents-only.md

@@ -9,12 +9,12 @@ ms.custom: seodec18
 ms.service: cognitive-services
 ms.subservice: language-understanding
 ms.topic: tutorial
-ms.date: 11/05/2019
+ms.date: 11/20/2019
 ms.author: diberry
 #Customer intent: As a new user, I want to create a custom app with intents, so that I can analyze user text in that subject domain.
 ---
 
-# Tutorial: Build LUIS app to determine user intentions
+# Tutorial: Build a LUIS app to determine user intentions
 
 In this tutorial, you create a custom app that predicts a user's intention based on the utterance (text). 
 
@@ -54,7 +54,7 @@ These intentions are categorized into **Intents**.
 
 ## Create a new intent 
 
-1. In the [preview LUIS portal](https://preview.luis.ai), inside the app's **Build** section, select **+ Create**. Enter the new intent name listed below then select **Done**.
+1. In the portal, inside the app's **Build** section, select **+ Create**. Enter the new intent name, `OrderPizza`, then select **Done**.
 
     The `OrderPizza` intent is predicted when: a user wants to order a pizza. 
 
@@ -118,7 +118,7 @@ These intentions are categorized into **Intents**.
 
     This is not exactly the same as an example utterance so it is a good test to see if LUIS can learn what should be predicted with this intent.
 
-    The last query string parameter is `q`, the utterance **query**. This utterance is not the same as any of the example utterances. It is a good test and should return the `OrderPizza` intent as the top scoring intent. 
+    The last query string parameter is `query`, the utterance **query**. This utterance is not the same as any of the example utterances. It is a good test and should return the `OrderPizza` intent as the top scoring intent. 
 
     ```JSON
     {
@@ -144,7 +144,7 @@ These intentions are categorized into **Intents**.
     }
     ```
 
-    The entities array is empty because this app currently does not have any entities. 
+    The entities array is empty because this app currently does not have any entities (unit of data inside the utterance to extract). 
 
     The JSON result identifies the top scoring intent as **`prediction.topIntent`** property. All scores are between 1 and 0, with the better score being closer to 1. 
 
@@ -201,4 +201,4 @@ After LUIS returns the JSON response, LUIS is done with this request. LUIS doesn
 This tutorial created a LUIS app, created intents, added example utterances to each intent, added example utterances to the None intent, trained, published, and tested at the endpoint. These are the basic steps of building a LUIS model. 
 
 > [!div class="nextstepaction"]
-> [Add prebuilt intents and entities to this app](tutorial-machine-learned-entity.md)
+> [Add a decomposable entity to this app](tutorial-machine-learned-entity.md)