Fix typos and improve clarity in best practices doc

Author: jfilcik

articles/ai-services/content-understanding/best-practices.md

@@ -1,5 +1,5 @@
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-title: Best practicies for using Content Understanding
+title: Best practices for using Content Understanding
 titleSuffix: Azure AI services
 description: Learn about how to best use Azure AI Content Understanding for content and field extractions on documents, images, video and audio files.
 author: jfilcik
@@ -21,7 +21,7 @@ This document provides guidance on how to effectively use Content Understanding
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 ## Use field descriptions to guide output
-When defining a schema, it is essential to provide detailed field descriptions. Clear and concise descriptions guide the model to focus on the correct information, improving the accuracy of the output.
+When defining a schema, it's essential to provide detailed field descriptions. Clear and concise descriptions guide the model to focus on the correct information, improving the accuracy of the output.
 
 ### Example 1:
 If you want to extract the date from an invoice, in addition to naming the field `"Date"`, provide a description such as:
@@ -45,26 +45,26 @@ This extra context guides the model to the right location in the document.
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 ## Use classification fields for specific outputs
-When you need the system to choose from a set of predefined options (e.g., document type, product category, or status), use classification fields. When there's ambiguity with the options, provide clear descriptions for each option, enabling the model to categorize the data accurately.
+When you need the system to choose from a set of predefined options (for example, document type, product category, or status), use classification fields. When there's ambiguity with the options, provide clear descriptions for each option, enabling the model to categorize the data accurately.
 
 ### Example 1:
 If you need to classify documents as either `"Invoice"`, `"Claim"`, or `"Report"`, create a classification field with these words as category names.
 
 ### Example 2:
 When processing product images, you might need to assign them to categories like `"AlcoholicDrinks"`, `"SoftDrinks"`, `"Snacks"`, and `"DairyProducts"`. Since some items may appear similar, providing precise definitions for close-call cases can help. For example:
 
-- **`"Alcoholic Drinks"`**: Beverages containing alcohol, such as beer, wine, and spirits. This excludes soft drinks or non-alcoholic beverages.
-- **`"Soft Drinks"`**: Carbonated non-alcoholic beverages, such as soda and sparkling water. This does not include juices or alcoholic drinks.
+- **`"Alcoholic Drinks"`**: Beverages containing alcohol, such as beer, wine, and spirits. This category excludes soft drinks or other nonalcoholic beverages..
+- **`"Soft Drinks"`**: Carbonated nonalcoholic beverages, such as soda and sparkling water. This category doesn't include juices or alcoholic drinks..
 
 By clearly defining each category, you ensure that the system correctly classifies products while minimizing misclassification.
 
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 ## Use confidence scores to determine when human review is needed
-Confidence scores help you decide when to involve human reviewers. Customers can interpret confidence scores using thresholds to decide which results need more review, minimizing the risk of errors.
+Confidence scores help you decide when to involve human reviewers. Customers can interpret confidence scores using thresholds to decide which results need more reviews, minimizing the risk of errors.
 
 ### Example:
-For an Invoice review use case, if a key extracted field like `"TotalInvoiceAmount"` has a confidence score under **0.80**, route that document to manual review. This ensures that critical fields like invoice totals or legal statements are verified by a human when necessary.
+For an Invoice review use case, if a key extracted field like `"TotalInvoiceAmount"` has a confidence score under **0.80**, route that document to manual review. This helps ensure that a human verifies critical fields like invoice totals or legal statements when necessary.
 
 You might set different confidence thresholds based on the type of field. For instance, a lower threshold for a `"Comments"` field that’s less critical and a higher one for `"ContractTerminationDate"` to ensure no mistakes.
 
@@ -73,12 +73,12 @@ You might set different confidence thresholds based on the type of field. For in
 ## Reduce errors by narrowing language selection for audio and video
 When working with audio and video content, selecting a narrow set of languages for transcription can potentially reduce errors. The more languages you include, the more the system has to guess which language is being spoken, which may increase misrecognition.
 
-### Example 1:
-If you are certain that the content only contains English and Spanish, configuring your transcription to these two languages only may improve quality.  But if the content accidentally includes another languages, such configuration may actually degrade overall quality.
+### Example:
+If you're certain that the content only contains English and Spanish, configuring your transcription to these two languages only may improve quality.  But if the content accidentally includes other languages, such configuration may actually degrade overall quality.
 
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-## Transcript, OCR text, and speaker data do not require fields
-By default, Content Extraction information such as transcripts, OCR results, and video key frames can be accessed directly from the analyzer output for immediate review or custom processing. There is no need to define a field in the schema for these items. Fields can be used when additional processing is needed (e.g., summarizing transcripts, identifying entities, or extracting specific items from OCR). Each field can instruct the system to extract or generate the content you need.
+## Transcript, document text, and speaker data don't require fields
+By default, Content Extraction information such as speech transcripts, document text extracted by OCR, and video key frames can be accessed directly from the analyzer output for immediate review or custom processing. There's no need to define a field in the schema for these items. Fields can be used when additional processing is needed (e.g., summarizing transcripts, identifying entities, or extracting specific items from OCR). Each field can instruct the system to extract or generate the content you need.
 
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