acrolinx

Author: ivorb

learn-pr/wwl-data-ai/create-question-answer-solution-ai-language/includes/6-test-publish-knowledge-base.md

@@ -5,8 +5,8 @@ After you have defined a knowledge base, you can train its natural language mode
 
 You can test your knowledge base interactively in Language Studio, submitting questions and reviewing the answers that are returned. You can inspect the results to view their confidence scores as well as other potential answers.
 
-:::image type="content" source="../media/test-new-small.png" alt-text="Screenshot of the test pane of the custom question answering project in the Language studio."lightbox="../media/test-new.png":::
+:::image type="content" source="../media/test-new-small.png" alt-text="Screenshot of the test pane of the custom question answering project in the Language studio.":::
 
 ## Deploying a knowledge base
 
-When you are happy with the performance of your knowledge base, you can deploy it to a REST endpoint that client applications can use to submit questions and receive answers. You can deploy it directly from Language Studio.
+When you're happy with the performance of your knowledge base, you can deploy it to a REST endpoint that client applications can use to submit questions and receive answers. You can deploy it directly from Language Studio.

learn-pr/wwl-data-ai/plan-form-recognizer-solution/includes/2-understand.md

@@ -48,7 +48,7 @@ The other prebuilt models expect a common type of form or document, such as:
 - Health insurance card
 
 > [!IMPORTANT]
-> This list shows a subset of the prebuilt models available. Check the Azure AI Document Intelligence documentation for [all available models](/azure/ai-services/document-intelligence/model-overview?view=doc-intel-4.0.0&azure-portal=true#model-overview).
+> This list shows a subset of the prebuilt models available. Check the Azure AI Document Intelligence documentation for [all available models](/azure/ai-services/document-intelligence/model-overview?azure-portal=true#model-overview).
 
 If you have an unusual or unique type of form, you can use the above general document analysis prebuilt models to extract information from them. However, if you want to extract more specific information than the prebuilt models support, you can create a **custom model** and train it by using examples of completed forms. 
 

learn-pr/wwl-data-ai/plan-form-recognizer-solution/includes/4-choose-model-type.md

@@ -6,7 +6,7 @@ In this unit, you'll learn about the prebuilt models available in Azure AI Docum
 
 ## Prebuilt models
 
-Document types such as invoices and receipts vary in different businesses and industry but have similar structures and key-value pairs. For example, a "Total cost" value is likely to appear on almost all invoices although it might be called "Total", "Sum", or some other name. Microsoft has provided a set of prebuilt models with Azure AI Document Intelligence to handle the most common types of documents. You don't have to train these models and you can create solutions using them very quickly.
+Document types such as invoices and receipts vary in different businesses and industry but have similar structures and key-value pairs. For example, a "Total cost" value is likely to appear on almost all invoices although it might be called "Total", "Sum", or some other name. Azure AI Document Intelligence provides a set of prebuilt models to handle the most common types of documents. You don't have to train these models and you can create solutions using them quickly.
 
 ### General document analysis models
 
@@ -50,9 +50,9 @@ The other prebuilt models are each designed to handle, and trained on, a specifi
 
 ## Custom models
 
-If the prebuilt models don't suit your purposes, you can create a custom model and train it to analyze the specific type of document users will send to your Azure AI Document Intelligence service. The general document analyzer prebuilt models can extract rich information from these forms and you might be able to use them if your requirements are to obtain general data. However, by using a custom model, trained on forms with similar structures and key-value pairs, you will obtain more predictable and standardized results from your unusual form types.
+If the prebuilt models don't suit your purposes, you can create a custom model and train it to analyze the specific type of document users will send to your Azure AI Document Intelligence service. The general document analyzer prebuilt models can extract rich information from these forms and you might be able to use them if your requirements are to obtain general data. However, by using a custom model, trained on forms with similar structures and key-value pairs, you'll obtain more predictable and standardized results from your unusual form types.
 
-To train a custom model, you must supply at least five examples of the completed form but the more examples you supply, the greater the confidence levels Azure AI Document Intelligence will return when it analyzes input. The more varied your documents are in terms of structure and terminology, the greater the number of example documents you will need to supply to train a reliable model. You can either supply a labeled dataset to describe the expected data or allow the model to identify key-value pairs and table data based on what it finds in the example forms. Also, make sure your training forms include examples that span the full range of possible input. For example, if you are expecting both hand-written and printed entries, include them both in your training.
+To train a custom model, you must supply at least five examples of the completed form but the more examples you supply, the greater the confidence levels Azure AI Document Intelligence will return when it analyzes input. The more varied your documents are in terms of structure and terminology, the greater the number of example documents you'll need to supply to train a reliable model. You can either supply a labeled dataset to describe the expected data or allow the model to identify key-value pairs and table data based on what it finds in the example forms. Also, make sure your training forms include examples that span the full range of possible input. For example, if you're expecting both hand-written and printed entries, include them both in your training.
 
 Once you have trained a custom model in this way, Azure AI Document Intelligence can accurately and predictably identify information in your unique forms.
 
@@ -65,7 +65,7 @@ There are two kinds of custom model:
 
 ## Composed models
 
-A composed model is one that consists of multiple custom models. Typical scenarios where composed models help are when you don't know the submitted document type and want to classify and then analyze it. They are also useful if you have multiple variations of a form, each with a trained individual model. When a user submits a form to the composed model, Document Intelligence automatically classifies it to determine which of the custom models should be used in its analysis. In this approach, a user doesn't have to know what kind of document it is before submission. That can be helpful when you're using lots of similar forms or when you want to publish a single endpoint for all your form types.
+A composed model is one that consists of multiple custom models. Typical scenarios where composed models help are when you don't know the submitted document type and want to classify and then analyze it. They're also useful if you have multiple variations of a form, each with a trained individual model. When a user submits a form to the composed model, Document Intelligence automatically classifies it to determine which of the custom models should be used in its analysis. In this approach, a user doesn't have to know what kind of document it is before submission. That can be helpful when you're using lots of similar forms or when you want to publish a single endpoint for all your form types.
 
 > [!IMPORTANT]
 > The results from a composed model include the `docType` property, which indicates the custom model that was chosen to analyze each form.