@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 10/03/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 monikerRange: '>=doc-intel-4.0.0'
 ---
@@ -53,24 +53,11 @@ A check is a secure way to transfer amount from payee's account to receiver's ac
 
 ## Supported languages and locales
 
-*See* our [Language Support](../language-support/prebuilt.md) page for a complete list of supported languages.
+ For a complete list of supported languages, *see* our [prebuilt model language support](../language-support/prebuilt.md) page.
 
 ## Field extractions
 
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`PayerName`|`string`|Name of the payer (drawer)|Jane Doe|
-|`PayerAddress`|`address`|Address of the payer (drawer)|123 Main St., Redmond, Washington, 98052|
-|`PayTo`|`string`|Name of the payee|John Smith|
-|`CheckDate`|`date`|Date the check was written|2023-04-01|
-|`NumberAmount`|`number`|Amount of the check written in numeric form|150.00|
-|`WordAmount`|`number`|Amount of the check written in letter form|one-hundred-fifty and 00/100|
-|`BankName`|`string`|Name of the bank|Contoso Bank|
-|`Memo`|`string`|Short note describing the payment|April Rent Payment|
-|`MICR`|`object`|Magnetic Ink Character Recognition (MICR) line|⑈0740⑈ ⑆123456789⑆ 1001001234⑈|
-|`MICR.RoutingNumber`|`string`|Routing number of the bank|⑆123456789⑆|
-|`MICR.AccountNumber`|`string`|Account number|1001001234⑈|
-|`MICR.CheckNumber`|`string`|Check number|⑈0740⑈|
+For supported document extraction fields, refer to the [bank check model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/bank-check.md) page in our GitHub sample repository.
 
 ## Supported locales
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 10/03/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 ms.custom: references_regions
 monikerRange: '>=doc-intel-4.0.0'
@@ -51,30 +51,11 @@ A bank statement helps review account's activities during a specified period. It
 
 ## Supported languages and locales
 
-*See* our [Language Support](../language-support/prebuilt.md) page for a complete list of supported languages.
+For a complete list of supported languages, *see* our [prebuilt model language support](../language-support/prebuilt.md) page.
 
 ## Field extractions
 
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`AccountNumber`|`string`|Account number on the bank statement|987-654-3210|
-|`AccountType`|`string`|Type of account on the bank statement|Checking|
-|`BankAddress`|`address`|Listed address of the bank|123 Main St., Redmond, Washington 98052|
-|`BankName`|`string`|Listed name of the bank|Contoso Bank|
-|`AccountHolderAddress`|`address`|Address of the account holder|456 Main St., Redmond, Washington 98052|
-|`AccountHolderName`|`string`|Name of the account holder|JOHN DOE|
-|`EndingBalance`|`number`|Ending balance on the bank statement|$1488.03|
-|`BeginningBalance`|`number`|Beginning balance on the bank statement|$1488.03|
-|`StatementStartDate`|`date`|Start date of the bank statement|July 01, 2017|
-|`StatementEndDate`|`date`|End date of the bank statement|July 31, 2017|
-|`TotalServiceFees`|`number`|Total service fees|$0.00|
-|`Transactions`|`array`|Extracted transaction line item|07/17<br>OnlineTransfer From Check...6609 Transaction#: 6373187418<br>$1,500.00|
-|`Transactions.*`|`object`|||
-|`Transactions.*.Date`|`date`|Transaction date|07/17|
-|`Transactions.*.Description`|`string`|Transaction description|OnlineTransfer From Check...6609 Transaction#: 6373187418|
-|`Transactions.*.CheckNumber`|`string`|Check number of the transaction|6609|
-|`Transactions.*.DepositAmount`|`number`|Amount of deposit in the transaction|$1500.00|
-|`Transactions.*.WithdrawalAmount`|`number`|Amount of withdrawal in the transaction|$1500.00|
+For supported document extraction fields, refer to the [bank check model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/bank-statement.md) page in our GitHub sample repository.
 
 ## Supported locales
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 05/23/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 ---
 
@@ -176,25 +176,11 @@ See how data, including name, job title, address, email, and company name, is ex
 
 ## Supported languages and locales
 
-*See* our [Language Support](../language-support/prebuilt.md) page for a complete list of supported languages.
+For a complete list of supported languages, *see* our [prebuilt model language support](../language-support/prebuilt.md) page.
 
 ## Field extractions
 
-|Name| Type | Description |Standardized output |
-|:-----|:----|:----|:----:|
-| ContactNames | Array of objects | Contact name |  |
-| FirstName | String | First (given) name of contact |  |
-| LastName | String | Last (family) name of contact |  |
-| CompanyNames | Array of strings | Company name|  |
-| Departments | Array of strings | Department or organization of contact |  |
-| JobTitles | Array of strings | Listed Job title of contact |  |
-| Emails | Array of strings | Contact email address |  |
-| Websites | Array of strings | Company website |  |
-| Addresses | Array of strings | Address extracted from business card | |
-| MobilePhones | Array of phone numbers | Mobile phone number from business card |+1 xxx xxx xxxx |
-| Faxes | Array of phone numbers | Fax phone number from business card | +1 xxx xxx xxxx |
-| WorkPhones | Array of phone numbers | Work phone number from business card | +1 xxx xxx xxxx |
-| OtherPhones     | Array of phone numbers | Other phone number from business card | +1 xxx xxx xxxx |
+For supported document extraction fields, refer to the [business card model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2023-07-31/business-card.md) page in our GitHub sample repository.
 
 ::: moniker-end
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 08/07/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 monikerRange: '>=doc-intel-3.0.0'
 ---
@@ -89,25 +89,13 @@ See how data, including customer information, vendor details, and line items, is
 
 ## Supported languages and locales
 
-*See* our [Language Support—prebuilt models](../language-support/prebuilt.md) page for a complete list of supported languages.
+For a complete list of supported languages, *see* our [Language Support—prebuilt models](../language-support/prebuilt.md) page.
 
 ## Field extraction
 
-The following are the fields extracted from a contract in the JSON output response. 
-
-|Name| Type | Description | Example output |
-|:-----|:----|:----|:---:|
-| Title | String | Contract title| Service agreement |
-| ContractId | String | Contract title| AB12956 |
-| Parties | Array |List of legal parties| |
-| ExecutionDate | Date |Date when the agreement was fully signed and agreed upon by all parties|`On this twenty-third day of February two thousand and twenty two` |
-| ExpirationDate | Date |Date when the contract ends| One year |
-| EffectiveDate  | Date |Date when the contract is in effect| immediately |
-| RenewalDate | Date |Date when the contract needs to be renewed| `On this twenty-third day of February two thousand and twenty two` |
-| ContractDuration | String | Contract terms | Five (5) years |
-| Jurisdictions | Array | List of jurisdictions| |
-
-The contract key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
+* For supported document extraction fields, refer to the [contract model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/contract.md) page in our GitHub sample repository.
+
+* The contract key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
 
 ## Next steps
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 10/03/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 monikerRange: '>=doc-intel-4.0.0'
 ---
@@ -68,25 +68,13 @@ To see how data extraction works for the Credit/Debit card service, you need the
 
 ## Supported languages and locales
 
-*See* our [Language Support—prebuilt models](../language-support/prebuilt.md) page for a complete list of supported languages.
+For a complete list of supported languages, *see* our [prebuilt model language support](../language-support/prebuilt.md) page.
 
 ## Field extraction
 
-The following are the fields extracted from a contract in the JSON output response.
-
-|Name| Type | Description | Example output |
-|:-----|:----|:----|:---:|
-| CardNumber | String | A unique identifier for the card| 4275 0000 0000 0000 |
-| IssuingBank | String | The name of the bank that issued the card| Woodgrove Bank |
-| PaymentNetwork | String |The payment network that processes the card transaction| VISA |
-| CardHolderName | String |The name of the person who owns the card| JOHN SMITH |
-| CardHolderCompanyName | String| The name of the company that the card is associated with | Contoso, Ltd. |
-| ValidDate | Date | Valid from date | 01/16 |
-| ExpirationDate | Date | Expiration date| 01/19 |
-| CardVerificationValue | String | Card verification value (CVV) | 764 |
-| CustomerServicePhoneNumbers | Array | List of support numbers | +1 (555) 123-4567 |
-
-The bank cards key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
+* For supported document extraction fields, refer to the [credit card model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/credit-card.md) page in our GitHub sample repository.
+
+* The bank cards key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
 
 ## Next steps
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 05/23/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 monikerRange: 'doc-intel-4.0.0 || >=doc-intel-3.0.0'
 ---
@@ -93,51 +93,11 @@ See how data is extracted from health insurance cards using the Document Intelli
 
 ## Supported languages and locales
 
-*See* our [Language Support—prebuilt models](../language-support/prebuilt.md) page for a complete list of supported languages.
+For a complete list of supported languages, *see* our [prebuilt model language support](../language-support/prebuilt.md) page.
 
 ## Field extraction
 
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`Insurer`|`string`|Health insurance provider name|PREMERA<br>BLUE CROSS|
-|`Member`|`object`|||
-|`Member.Name`|`string`|Member name|ANGEL BROWN|
-|`Member.BirthDate`|`date`|Member date of birth|01/06/1958|
-|`Member.Employer`|`string`|Member name employer|Microsoft|
-|`Member.Gender`|`string`|Member gender|M|
-|`Member.IdNumberSuffix`|`string`|Identification Number Suffix as it appears on some health insurance cards|01|
-|`Dependents`|`array`|Array holding list of dependents, ordered where possible by membership suffix value||
-|`Dependents.*`|`object`|||
-|`Dependents.*.Name`|`string`|Dependent name|01|
-|`IdNumber`|`object`|||
-|`IdNumber.Prefix`|`string`|Identification Number Prefix as it appears on some health insurance cards|`ABC`|
-|`IdNumber.Number`|`string`|Identification Number|123456789|
-|`GroupNumber`|`string`|Insurance Group Number|1000000|
-|`PrescriptionInfo`|`object`|||
-|`PrescriptionInfo.Issuer`|`string`|ANSI issuer identification number (IIN)|(80840) 300-11908-77|
-|`PrescriptionInfo.RxBIN`|`string`|Prescription issued BIN number|987654|
-|`PrescriptionInfo.RxPCN`|`string`|Prescription processor control number|63200305|
-|`PrescriptionInfo.RxGrp`|`string`|Prescription group number|BCAAXYZ|
-|`PrescriptionInfo.RxId`|`string`|Prescription identification number. If not present, defaults to membership ID number|P97020065|
-|`PrescriptionInfo.RxPlan`|`string`|Prescription Plan number|A1|
-|`Pbm`|`string`|Pharmacy Benefit Manager for the plan|CVS CAREMARK|
-|`EffectiveDate`|`date`|Date from which the plan is effective|08/12/2012|
-|`Copays`|`array`|Array holding list of Co-Pay Benefits||
-|`Copays.*`|`object`|||
-|`Copays.*.Benefit`|`string`|Co-Pay Benefit name|Deductible|
-|`Copays.*.Amount`|`currency`|Co-Pay required amount|$1,500|
-|`Payer`|`object`|||
-|`Payer.Id`|`string`|Payer ID Number|89063|
-|`Payer.Address`|`address`|Payer address|123 Service St., Redmond, Washington, 98052|
-|`Payer.PhoneNumber`|`phoneNumber`|Payer phone number|+1 (987) 213-5674|
-|`Plan`|`object`|||
-|`Plan.Number`|`string`|Plan number|456|
-|`Plan.Name`|`string`|Plan name - If Medicaid -> then `medicaid` (all lower case).|HEALTH SAVINGS PLAN|
-|`Plan.Type`|`string`|Plan type|`PPO`|
-|`MedicareMedicaidInfo`|`object`|||
-|`MedicareMedicaidInfo.Id`|`string`|Medicare or Medicaid number|1AB2-CD3-EF45|
-|`MedicareMedicaidInfo.PartAEffectiveDate`|`date`|Effective date of Medicare Part A|01-01-2023|
-|`MedicareMedicaidInfo.PartBEffectiveDate`|`date`|Effective date of Medicare Part B|01-01-2023|
+For supported document extraction fields, refer to the [health insurance card model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/health-insurance-card.md) page in our GitHub sample repository.
 
 ### Migration guide and REST API v3.1
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 08/07/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 ms.custom:
   - references.regions
@@ -228,145 +228,12 @@ Extract data, including name, birth date, and expiration date, from ID documents
 
 ## Field extractions
 
-The following are the fields extracted per document type. The Document Intelligence ID model `prebuilt-idDocument` extracts the following fields in the `documents.*.fields`. The json output includes all the extracted text in the documents, words, lines, and styles.
-
-::: moniker-end
-
-::: moniker range="doc-intel-3.1.0"
-
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Prebuilt_model/sample_analyze_identity_documents.py)
-
-::: moniker-end
-
-::: moniker range="doc-intel-4.0.0"
-
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Prebuilt_model/sample_analyze_identity_documents.py)
+For supported document extraction fields, refer to the [ID document model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/id-document.md) page in our GitHub sample repository.
 
 ::: moniker-end
 
 <!--docutune:disable -->
 
-::: moniker range=">=doc-intel-3.0.0"
-
-### `idDocument.driverLicense`
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`CountryRegion`|`countryRegion`|Country or region code|USA|
-|`Region`|`string`|State or province|Washington|
-|`DocumentNumber`|`string`|Driver license number|WDLABCD456DG|
-|`DocumentDiscriminator`|`string`|Driver license document discriminator|12645646464554646456464544|
-|`FirstName`|`string`|Given name and middle initial if applicable|LIAM R.|
-|`LastName`|`string`|Surname|TALBOT|
-|`Address`|`address`|Address|123 STREET ADDRESS YOUR CITY WA 99999-1234|
-|`DateOfBirth`|`date`|Date of birth|01/06/1958|
-|`DateOfExpiration`|`date`|Date of expiration|08/12/2020|
-|`DateOfIssue`|`date`|Date of issue|08/12/2012|
-|`EyeColor`|`string`|Eye color|BLU|
-|`HairColor`|`string`|Hair color|BRO|
-|`Height`|`string`|Height|5'11"|
-|`Weight`|`string`|Weight|185LB|
-|`Sex`|`string`|Sex|M|
-|`Endorsements`|`string`|Endorsements|L|
-|`Restrictions`|`string`|Restrictions|B|
-|`PersonalNumber`|`string`|Personal Id. No.|A234567893|
-|`PlaceOfBirth`|`string`|Place of birth|MASSACHUSETTS, U.S.A.|
-|`VehicleClassifications`|`string`|Vehicle classification|D|
-
-### `idDocument.passport`
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`DocumentNumber`|`string`|Passport number|340020013|
-|`FirstName`|`string`|Given name and middle initial if applicable|JENNIFER|
-|`MiddleName`|`string`|Name between given name and surname|REYES|
-|`LastName`|`string`|Surname|BROOKS|
-|`Aliases`|`array`|||
-|`Aliases.*`|`string`|Also known as|MAT LIN|
-|`DateOfBirth`|`date`|Date of birth|1980-01-01|
-|`DateOfExpiration`|`date`|Date of expiration|2019-05-05|
-|`DateOfIssue`|`date`|Date of issue|2014-05-06|
-|`Sex`|`string`|Sex|F|
-|`CountryRegion`|`countryRegion`|Issuing country or organization|USA|
-|`DocumentType`|`string`|Document type|P|
-|`Nationality`|`countryRegion`|Nationality|USA|
-|`PlaceOfBirth`|`string`|Place of birth|MASSACHUSETTS, U.S.A.|
-|`PlaceOfIssue`|`string`|Place of issue|LISBON|
-|`IssuingAuthority`|`string`|Issuing authority|United States Department of State|
-|`PersonalNumber`|`string`|Personal ID. No.|A234567893|
-|`MachineReadableZone`|`object`|Machine readable zone (MRZ)|P<USABROOKS<<JENNIFER<<<<<<<<<<<<<<<<<<<<<<< 3400200135USA8001014F1905054710000307<715816|
-|`MachineReadableZone.FirstName`|`string`|Given name and middle initial if applicable|JENNIFER|
-|`MachineReadableZone.LastName`|`string`|Surname|BROOKS|
-|`MachineReadableZone.DocumentNumber`|`string`|Passport number|340020013|
-|`MachineReadableZone.CountryRegion`|`countryRegion`|Issuing country or organization|USA|
-|`MachineReadableZone.Nationality`|`countryRegion`|Nationality|USA|
-|`MachineReadableZone.DateOfBirth`|`date`|Date of birth|1980-01-01|
-|`MachineReadableZone.DateOfExpiration`|`date`|Date of expiration|2019-05-05|
-|`MachineReadableZone.Sex`|`string`|Sex|F|
-
-### `idDocument.nationalIdentityCard`
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`CountryRegion`|`countryRegion`|Country or region code|USA|
-|`Region`|`string`|State or province|Washington|
-|`DocumentNumber`|`string`|National identity card number|WDLABCD456DG|
-|`DocumentDiscriminator`|`string`|National identity card document discriminator|12645646464554646456464544|
-|`FirstName`|`string`|Given name and middle initial if applicable|LIAM R.|
-|`LastName`|`string`|Surname|TALBOT|
-|`Address`|`address`|Address|123 STREET ADDRESS YOUR CITY WA 99999-1234|
-|`DateOfBirth`|`date`|Date of birth|01/06/1958|
-|`DateOfExpiration`|`date`|Date of expiration|08/12/2020|
-|`DateOfIssue`|`date`|Date of issue|08/12/2012|
-|`EyeColor`|`string`|Eye color|BLUE|
-|`HairColor`|`string`|Hair color|BROWN|
-|`Height`|`string`|Height|5'11"|
-|`Weight`|`string`|Weight|185LB|
-|`Sex`|`string`|Sex|M|
-|`PersonalNumber`|`string`|Personal Id. No.|A234567893|
-|`PlaceOfBirth`|`string`|Place of birth|MASSACHUSETTS, U.S.A.|
-
-### `idDocument.residencePermit`
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`CountryRegion`|`countryRegion`|Country or region code|USA|
-|`DocumentNumber`|`string`|Residence permit number|WDLABCD456DG|
-|`FirstName`|`string`|Given name and middle initial if applicable|LIAM R.|
-|`LastName`|`string`|Surname|TALBOT|
-|`DateOfBirth`|`date`|Date of birth|01/06/1958|
-|`DateOfExpiration`|`date`|Date of expiration|08/12/2020|
-|`DateOfIssue`|`date`|Date of issue|08/12/2012|
-|`Sex`|`string`|Sex|M|
-|`PersonalNumber`|`string`|Personal Id. No.|A234567893|
-|`PlaceOfBirth`|`string`|Place of birth|Germany|
-|`Category`|`string`|Permit category|DV2|
-|`Address`|`string`|Address|123 STREET ADDRESS YOUR CITY WA 99999-1234|
-
-### `idDocument.usSocialSecurityCard`
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`DocumentNumber`|`string`|Social security card number|WDLABCD456DG|
-|`FirstName`|`string`|Given name and middle initial if applicable|LIAM R.|
-|`LastName`|`string`|Surname|TALBOT|
-|`DateOfIssue`|`date`|Date of issue|08/12/2012|
-
-### `idDocument`
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`Address`|`address`|Address|123 STREET ADDRESS YOUR CITY WA 99999-1234|
-|`DocumentNumber`|`string`|Driver license number|WDLABCD456DG|
-|`FirstName`|`string`|Given name and middle initial if applicable|LIAM R.|
-|`LastName`|`string`|Surname|TALBOT|
-|`DateOfBirth`|`date`|Date of birth|01/06/1958|
-|`DateOfExpiration`|`date`|Date of expiration|08/12/2020|
-
-::: moniker-end
-
 ::: moniker range="doc-intel-2.1.0"
 
 <!--docutune:enable -->
@@ -387,7 +254,7 @@ The following are the fields extracted per document type. The Document Intellige
 |  LastName | string | Extracted surname | "BROOKS" |
 |  Nationality | country | Country code compliant with ISO 3166 standard | "USA" |
 |  Sex | gender | Possible extracted values include "M" "F" "X" | "F" |
-|  MachineReadableZone | object | Extracted Passport MRZ including two lines of 44 characters each | "P<USABROOKS<<JENNIFER<<<<<<<<<<<<<<<<<<<<<<< 3400200135USA8001014F1905054710000307<715816" |
+|  MachineReadableZone | object | Extracted Passport `MRZ` including two lines of 44 characters each | "P<USABROOKS<<JENNIFER<<<<<<<<<<<<<<<<<<<<<<< 3400200135USA8001014F1905054710000307<715816" |
 |  DocumentType | string | Document type, for example, Passport, Driver's License | "passport" |
 |  Address | string | Extracted address (Driver's License only) | "123 STREET ADDRESS YOUR CITY WA 99999-1234"|
 |  Region | string | Extracted region, state, province, etc. (Driver's License only) | "Washington" |

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 08/07/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 ms.custom: references_regions
 ---
@@ -44,7 +44,7 @@ The Document Intelligence invoice model uses powerful Optical Character Recognit
 
 ## Automated invoice processing
 
-Automated invoice processing is the process of extracting key accounts payable fields from billing account documents. Extracted data includes line items from invoices integrated with your accounts payable (AP) workflows for reviews and payments. Historically, the accounts payable process is performed manually and, hence, very time consuming. Accurate extraction of key data from invoices is typically the first and one of the most critical steps in the invoice automation process.
+Automated invoice processing is the process of extracting key `accounts payable` fields from billing account documents. Extracted data includes line items from invoices integrated with your accounts payable (AP) workflows for reviews and payments. Historically, the accounts payable process is performed manually and, hence, very time consuming. Accurate extraction of key data from invoices is typically the first and one of the most critical steps in the invoice automation process.
 
 ::: moniker range=">=doc-intel-3.0.0"
 
@@ -183,82 +183,17 @@ See how data, including customer information, vendor details, and line items, is
 
 ## Supported languages and locales
 
-*See* our [Language Support—prebuilt models](../language-support/prebuilt.md) page for a complete list of supported languages.
+For a complete list of supported languages, *see* our [prebuilt model language support](../language-support/prebuilt.md) page.
 
 ## Field extraction
-The Document Intelligence invoice model `prebuilt-invoice` extracts the following fields.
 
-::: moniker range="doc-intel-3.1.0"
-
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Prebuilt_model/sample_analyze_invoices.py)
 
-::: moniker-end
-
-::: moniker range="doc-intel-4.0.0"
+:::moniker range=">=doc-intel-3.1.0"
 
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Prebuilt_model/sample_analyze_invoices.py)
+* For supported document extraction fields, refer to the [invoice model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/invoice.md) page in our GitHub sample repository.
 
-::: moniker-end
+* The invoice key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
 
-|Name| Type | Description | Standardized output |
-|:-----|:----|:----|:----|
-| CustomerName |string | Invoiced customer|Microsoft Corp|
-| CustomerId |string | Customer reference ID |CID-12345 |
-| PurchaseOrder |string | Purchase order reference number |PO-3333 |
-| InvoiceId |string | ID for this specific invoice (often Invoice Number) |INV-100 |
-| InvoiceDate |date |date the invoice was issued | mm-dd-yyyy|
-| DueDate |date |date payment for this invoice is due |mm-dd-yyyy|
-| VendorName |string | Vendor who created this invoice |CONTOSO|
-| VendorAddress |address|  Vendor mailing address| 123 456th St, New York, NY 10001 |
-| VendorAddressRecipient |string | Name associated with the VendorAddress |Contoso Headquarters  |
-| CustomerAddress |address | Mailing address for the Customer | 123 Other St, Redmond, WA, 98052|
-| CustomerAddressRecipient |string | Name associated with the CustomerAddress |Microsoft Corp |
-| BillingAddress |address | Explicit billing address for the customer | 123 Bill St., Redmond, WA, 98052 |
-| BillingAddressRecipient |string | Name associated with the BillingAddress |Microsoft Services |
-| ShippingAddress |address | Explicit shipping address for the customer | 123 Ship St., Redmond, WA, 98052|
-| ShippingAddressRecipient |string | Name associated with the ShippingAddress |Microsoft Delivery  |
-|Sub​Total| currency| Subtotal field identified on this invoice | $100.00 |
-| TotalDiscount | currency | The total discount applied to an invoice | $5.00 |
-| TotalTax | currency| Total tax field identified on this invoice | $10.00 |
-| InvoiceTotal | currency | Total new charges associated with this invoice | $10.00 |
-| AmountDue |  currency | Total Amount Due to the vendor | $610 |
-| PreviousUnpaidBalance | currency| Explicit previously unpaid balance | $500.00 |
-| RemittanceAddress |address| Explicit remittance or payment address for the customer |123 Remit St New York, NY, 10001   |
-| RemittanceAddressRecipient |string | Name associated with the RemittanceAddress |Contoso Billing |
-| ServiceAddress |address | Explicit service address or property address for the customer |123 Service St., Redmond WA, 98052 |
-| ServiceAddressRecipient |string | Name associated with the ServiceAddress |Microsoft Services  |
-| ServiceStartDate |date | First date for the service period (for example, a utility bill service period) | mm-dd-yyyy |
-| ServiceEndDate |date | End date for the service period (for example, a utility bill service period) | mm-dd-yyyy|
-| VendorTaxId |string | The taxpayer number associated with the vendor |123456-7 |
-|CustomerTaxId|string|The taxpayer number associated with the customer|765432-1|
-| PaymentTerm |string | The terms of payment for the invoice |Net 90 |
-| KVKNumber |string | A unique identifier for businesses registered in the Netherlands (NL-only)|12345678|
-| CurrencyCode |string | The currency code associated with the extracted amount | |
-| PaymentDetails | array | An array that holds Payment Option details such as `IBAN`,`SWIFT`, `BPayBillerCode(AU)`, `BPayReference(AU)` |  |
-|TaxDetails|array|An array that holds tax details like amount and rate||
-| TaxDetails | array | AN array that holds added tax information such as `CGST`, `IGST`, and `SGST`. This line item is currently only available for the Germany (`de`), Spain (`es`), Portugal (`pt`), and English Canada (`en-CA`) locales| |
-
-### Line items array
-
-Following are the line items extracted from an invoice in the JSON output response (the following output uses this [sample invoice](../media/sample-invoice.jpg):
-
-|Name| Type | Description | Value (standardized output) |
-|:-----|:----|:----|:----|
-| Amount | currency | The amount of the line item | $60.00 |
-| Date | date| Date corresponding to each line item. Often it's a date the line item was shipped | 3/4/2021|
-| Description | string | The text description for the invoice line item | Consulting service|
-| Quantity | number | The quantity for this invoice line item | 2 |
-| ProductCode | string| Product code, product number, or SKU associated with the specific line item | A123|
-| Tax | currency | Tax associated with each line item. Possible values include tax amount and tax Y/N | $6.00 |
-| TaxRate | string | Tax Rate associated with each line item. | 18%|
-| Unit | string| The unit of the line item, e.g,  kg, lb etc. | Hours|
-| UnitPrice | number | The net or gross price (depending on the gross invoice setting of the invoice) of one unit of this item | $30.00 |
-
-The invoice key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
-
-:::moniker range=">=doc-intel-3.1.0"
 
 ### Key-value pairs
 
@@ -334,8 +269,8 @@ List all the detected payment options detected on the field.
 
 |Name| Type | Description | Text (line item #1) | Value (standardized output) |
 |:-----|:----|:----|:----| :----|
-| IBAN | string | Internal Bank Account Number | GB33BUKB20201555555555 | |
-| SWIFT | string | SWIFT code | BUKBGB22 | |
+| `IBAN` | string | Internal Bank Account Number | GB33BUKB20201555555555 | |
+| `SWIFT` | string | SWIFT code | BUKBGB22 | |
 | BankAccountNumber | string | Bank account number, a unique identifier for a bank account | 123456 | |
 | BPayBillerCode | string | Australian B-Pay Biller Code | 12345 | |
 | BPayReference | string | Australian B-Pay Reference Code | 98765432100 | |

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 10/03/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 monikerRange: '>=doc-intel-4.0.0'
 ---
@@ -68,35 +68,13 @@ To see how data extraction works for the marriage certificate card service, you
 
 ## Supported languages and locales
 
-*See* our [Language Support—prebuilt models](../language-support/prebuilt.md) page for a complete list of supported languages.
+For a complete list of supported languages, *see* our [prebuilt model language support](../language-support/prebuilt.md) page.
 
 ## Field extraction
 
-The following are the fields extracted from a marriage certificate  in the JSON output response.
-
-|Name| Type | Description | Example output |
-|:-----|:----|:----|:---:|
-| `Spouse1FirstName` | String | Spouse 1's first name| Wesley |
-| `Spouse1MiddleName` | String | Spouse 1's middle name| M. |
-| `Spouse1LastName` | String | Spouse 1's surname| Perry |
-| `Spouse1Age` | Integer | Spouse 1's age| 26 |
-| `Spouse1BirthDate` | Date | Spouse 1's birth date| Nov. 16, 1997 |
-| `Spouse1Address` | Address |Spouse 1's address| 4292 Don Jackson Lane, Bloomfield Township, Michigan 48302 |
-| `Spouse1BirthPlace`| String | Spouse 1's birth place| Michigan |
-| `Spouse2FirstName` | String | Spouse 2's first name| Beth |
-| `Spouse2MiddleName` | String | Spouse 2's middle name| R. |
-| `Spouse2LastName` | String | Spouse 2's surname| Mason |
-| `Spouse2Age` | Integer | Spouse 2's age| 23 |
-| `Spouse2BirthDate` | Date | Spouse 2's birth date| Jul. 22, 2000 |
-| `Spouse2Address` | Address |Spouse 2's address| 2671 Comfort Court, Madison, Wisconsin 53704 |
-| `Spouse2BirthPlace`| String | Spouse 2's birth place| Wisconsin |
-| `DocumentNumber`| String | Document number | 01976/202 |
-| `IssueDate` | Date | Issue date of the certificate | Oct. 10, 2023 |
-| `IssuePlace` | String | Issue place of the certificate | 2398 Echo Lane, Hastings, Michigan 49058 |
-| `MarriageDate`| Date | Marriage date | Oct. 10, 2023 |
-| `MarriagePlace` | String | Marriage place | 105 Coal Street, Galloway, Wisconsin 54432 |
-
-The marriage certificate key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
+* For supported document extraction fields, refer to the [marriage certificate model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/marriage-certificate.md) page in our GitHub sample repository.
+
+* The marriage certificate key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
 
 ## Next steps
 

@@ -1,5 +1,5 @@
 ---
-title: Document Intelligence pay stub model
+title: Document Intelligence payStub model
 titleSuffix: Azure AI services
 description: Automate compensation and earnings information from pay slips and stubs.
 author: laujan
@@ -13,15 +13,15 @@ monikerRange: '>=doc-intel-4.0.0'
 
 <!-- markdownlint-disable MD033 -->
 
-# Document Intelligence pay stub model
+# Document Intelligence payStub model
 
-The Document Intelligence pay stub model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract compensation and earnings data from pay slips. The API analyzes documents and files with payroll related information; extracts key information and returns a structured JSON data representation.
+The Document Intelligence payStub model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract compensation and earnings data from pay slips. The API analyzes documents and files with payroll related information; extracts key information and returns a structured JSON data representation.
 
 | Feature   | version| Model ID |
 |----------  |---------|--------|
-|Pay stub model|&bullet; v4.0:2024-07-31 (preview)|**`prebuilt-payStub.us`**|
+|payStub model|&bullet; v4.0:2024-07-31 (preview)|**`prebuilt-payStub.us`**|
 
-## Try pay stub data extraction
+## Try payStub data extraction
 
 Pay stubs are essential documents issued by employers to employees, providing earnings, deductions, and net pay information for a specific pay period. See how data is extracted using `prebuilt-payStub.us` model. You need the following resources:
 
@@ -45,28 +45,11 @@ Pay stubs are essential documents issued by employers to employees, providing ea
 
 ## Supported languages and locales
 
-*See* our [Language Support](../language-support/prebuilt.md) page for a complete list of supported languages.
+For a complete list of supported languages, *see* our [prebuilt model language support](../language-support/prebuilt.md) page.
 
 ## Field extractions
 
-|Name| Type | Description |Standardized output |
-|:-----|:----|:----|:----:|
-|`EmployeeAddress`|`address`|Address of the employee|123 Maple Street, Springfield, IL, 62701|
-|`EmployeeName`|`string`|Name of the employee|John A. Doe|
-|`EmployeeSSN`|`string`|Social security number of the employee|123-45-6789|
-|`EmployerAddress`|`address`|Address of the employer|456 Oak Avenue, Metropolis, NY, 10101|
-|`EmployerName`|`string`|Listed name of the employer|Contoso Corporation|
-|`PayDate`|`date`|Date of salary payment|Feb. 26, 2020|
-|`PayPeriodStartDate`|`date`|Start date of the pay period|Feb. 19, 2020|
-|`PayPeriodEndDate`|`date`|End date of the pay period|Feb. 25, 2020|
-|`CurrentPeriodGrossPay`|`number`|Gross pay of the current period|$744.10|
-|`YearToDateGrossPay`|`number`|Year-to-date gross pay|$2744.10|
-|`CurrentPeriodTaxes`|`number`|Taxes of the current period|$410.10|
-|`YearToDateTaxes`|`number`|Year-to-date taxes|$855.90|
-|`CurrentPeriodDeductions`|`number`|Deductions of the current period|$410.10|
-|`YearToDateDeductions`|`number`|Year-to-date deductions|$855.90|
-|`CurrentPeriodNetPay`|`number`|Net pay of the current period|$744.10|
-|`YearToDateNetPay`|`number`|Year-to-date net pay|$2744.10|
+For supported document extraction fields, refer to the [payStub model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/pay-stub.md) page in our GitHub sample repository.
 
 ## Supported locales
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 05/23/2024
+ms.date: 10/16/2024
 ms.author: lajanuar
 ---
 
@@ -195,7 +195,7 @@ See how Document Intelligence extracts data, including time and date of transact
 
 ## Supported languages and locales
 
-*See* our [Language Support—prebuilt models](../language-support/prebuilt.md) page for a complete list of supported languages.
+For a complete list of supported languages, *see* our [prebuilt models language support](../language-support/prebuilt.md) page.
 
 ## Field extraction
 
@@ -223,177 +223,10 @@ See how Document Intelligence extracts data, including time and date of transact
 
 ::: moniker range=">=doc-intel-3.0.0"
 
- Document Intelligence v3.0 and later versions introduce several new features and capabilities. In addition to thermal receipts, the **Receipt** model supports single-page hotel receipt processing and tax detail extraction for all receipt types.
-
- Document Intelligence v4.0 and later versions introduces support for currency for all price-related fields for thermal and hotel receipts.
+For supported document extraction fields, refer to the [receipt model schema](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/receipt.md) page in our GitHub sample repository.
  
 ::: moniker-end
 
-::: moniker range="doc-intel-3.1.0"
-
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Prebuilt_model/sample_analyze_receipts.py)
-
-::: moniker-end
-
-::: moniker range="doc-intel-4.0.0"
-
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Prebuilt_model/sample_analyze_receipts.py)
-
-::: moniker-end
-
-::: moniker range=">=doc-intel-3.0.0"
-
-### Receipt
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`MerchantName`|`string`|Name of the merchant issuing the receipt|Contoso|
-|`MerchantPhoneNumber`|`phoneNumber`|Listed phone number of merchant|987-654-3210|
-|`MerchantAddress`|`address`|Listed address of merchant|123 Main St. Redmond, Washington 98052|
-|`Total`|`number`|Full transaction total of receipt|$14.34|
-|`TransactionDate`|`date`|Date the receipt was issued|June 06, 2019|
-|`TransactionTime`|`time`|Time the receipt was issued|4:49 PM|
-|`Subtotal`|`number`|Subtotal of receipt, often before taxes are applied|$12.34|
-|`TotalTax`|`number`|Tax on receipt, often sales tax, or equivalent|$2.00|
-|`Tip`|`number`|Tip included by buyer|$1.00|
-|`Items`|`array`|||
-|`Items.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`Items.*.TotalPrice`|`number`|Total price of line item|$999.00|
-|`Items.*.Description`|`string`|Item description|Surface Pro 6|
-|`Items.*.Quantity`|`number`|Quantity of each item|1|
-|`Items.*.Price`|`number`|Individual price of each item unit|$999.00|
-|`Items.*.ProductCode`|`string`|Product code, product number, or SKU associated with the specific line item|A123|
-|`Items.*.QuantityUnit`|`string`|Quantity unit of each item||
-|`TaxDetails`|`array`|||
-|`TaxDetails.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`TaxDetails.*.Amount`|`currency`|The amount of the tax detail|$999.00|
-
-### receipt.retailMeal
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`MerchantName`|`string`|Name of the merchant issuing the receipt|Contoso|
-|`MerchantPhoneNumber`|`phoneNumber`|Listed phone number of merchant|987-654-3210|
-|`MerchantAddress`|`address`|Listed address of merchant|123 Main St. Redmond, Washington 98052|
-|`Total`|`number`|Full transaction total of receipt|$14.34|
-|`TransactionDate`|`date`|Date the receipt was issued|June 06, 2019|
-|`TransactionTime`|`time`|Time the receipt was issued|4:49 PM|
-|`Subtotal`|`number`|Subtotal of receipt, often before taxes are applied|$12.34|
-|`TotalTax`|`number`|Tax on receipt, often sales tax, or equivalent|$2.00|
-|`Tip`|`number`|Tip included by buyer|$1.00|
-|`Items`|`array`|||
-|`Items.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`Items.*.TotalPrice`|`number`|Total price of line item|$999.00|
-|`Items.*.Description`|`string`|Item description|Surface Pro 6|
-|`Items.*.Quantity`|`number`|Quantity of each item|1|
-|`Items.*.Price`|`number`|Individual price of each item unit|$999.00|
-|`Items.*.ProductCode`|`string`|Product code, product number, or SKU associated with the specific line item|A123|
-|`Items.*.QuantityUnit`|`string`|Quantity unit of each item||
-|`TaxDetails`|`array`|||
-|`TaxDetails.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`TaxDetails.*.Amount`|`currency`|The amount of the tax detail|$999.00|
-
-### receipt.creditCard
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`MerchantName`|`string`|Name of the merchant issuing the receipt|Contoso|
-|`MerchantPhoneNumber`|`phoneNumber`|Listed phone number of merchant|987-654-3210|
-|`MerchantAddress`|`address`|Listed address of merchant|123 Main St. Redmond, Washington 98052|
-|`Total`|`number`|Full transaction total of receipt|$14.34|
-|`TransactionDate`|`date`|Date the receipt was issued|June 06, 2019|
-|`TransactionTime`|`time`|Time the receipt was issued|4:49 PM|
-|`Subtotal`|`number`|Subtotal of receipt, often before taxes are applied|$12.34|
-|`TotalTax`|`number`|Tax on receipt, often sales tax, or equivalent|$2.00|
-|`Tip`|`number`|Tip included by buyer|$1.00|
-|`Items`|`array`|||
-|`Items.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`Items.*.TotalPrice`|`number`|Total price of line item|$999.00|
-|`Items.*.Description`|`string`|Item description|Surface Pro 6|
-|`Items.*.Quantity`|`number`|Quantity of each item|1|
-|`Items.*.Price`|`number`|Individual price of each item unit|$999.00|
-|`Items.*.ProductCode`|`string`|Product code, product number, or SKU associated with the specific line item|A123|
-|`Items.*.QuantityUnit`|`string`|Quantity unit of each item||
-|`TaxDetails`|`array`|||
-|`TaxDetails.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`TaxDetails.*.Amount`|`currency`|The amount of the tax detail|$999.00|
-
-### receipt.gas
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`MerchantName`|`string`|Name of the merchant issuing the receipt|Contoso|
-|`MerchantPhoneNumber`|`phoneNumber`|Listed phone number of merchant|987-654-3210|
-|`MerchantAddress`|`address`|Listed address of merchant|123 Main St. Redmond, Washington 98052|
-|`Total`|`number`|Full transaction total of receipt|$14.34|
-|`TransactionDate`|`date`|Date the receipt was issued|June 06, 2019|
-|`TransactionTime`|`time`|Time the receipt was issued|4:49 PM|
-|`Subtotal`|`number`|Subtotal of receipt, often before taxes are applied|$12.34|
-|`TotalTax`|`number`|Tax on receipt, often sales tax, or equivalent|$2.00|
-|`Tip`|`number`|Tip included by buyer|$1.00|
-|`Items`|`array`|||
-|`Items.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`Items.*.TotalPrice`|`number`|Total price of line item|$999.00|
-|`Items.*.Description`|`string`|Item description|Surface Pro 6|
-|`Items.*.Quantity`|`number`|Quantity of each item|1|
-|`Items.*.Price`|`number`|Individual price of each item unit|$999.00|
-|`Items.*.ProductCode`|`string`|Product code, product number, or SKU associated with the specific line item|A123|
-|`Items.*.QuantityUnit`|`string`|Quantity unit of each item||
-|`TaxDetails`|`array`|||
-|`TaxDetails.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`TaxDetails.*.Amount`|`currency`|The amount of the tax detail|$999.00|
-
-### receipt.parking
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`MerchantName`|`string`|Name of the merchant issuing the receipt|Contoso|
-|`MerchantPhoneNumber`|`phoneNumber`|Listed phone number of merchant|987-654-3210|
-|`MerchantAddress`|`address`|Listed address of merchant|123 Main St. Redmond, Washington 98052|
-|`Total`|`number`|Full transaction total of receipt|$14.34|
-|`TransactionDate`|`date`|Date the receipt was issued|June 06, 2019|
-|`TransactionTime`|`time`|Time the receipt was issued|4:49 PM|
-|`Subtotal`|`number`|Subtotal of receipt, often before taxes are applied|$12.34|
-|`TotalTax`|`number`|Tax on receipt, often sales tax, or equivalent|$2.00|
-|`Tip`|`number`|Tip included by buyer|$1.00|
-|`Items`|`array`|||
-|`Items.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`Items.*.TotalPrice`|`number`|Total price of line item|$999.00|
-|`Items.*.Description`|`string`|Item description|Surface Pro 6|
-|`Items.*.Quantity`|`number`|Quantity of each item|1|
-|`Items.*.Price`|`number`|Individual price of each item unit|$999.00|
-|`Items.*.ProductCode`|`string`|Product code, product number, or SKU associated with the specific line item|A123|
-|`Items.*.QuantityUnit`|`string`|Quantity unit of each item||
-|`TaxDetails`|`array`|||
-|`TaxDetails.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`TaxDetails.*.Amount`|`currency`|The amount of the tax detail|$999.00|
-
-### receipt.hotel
-
-| Field | Type | Description | Example |
-|:------|:-----|:------------|:--------|
-|`MerchantName`|`string`|Name of the merchant issuing the receipt|Contoso|
-|`MerchantPhoneNumber`|`phoneNumber`|Listed phone number of merchant|987-654-310|
-|`MerchantAddress`|`address`|Listed address of merchant|123 Main St. Redmond, Washington 98052|
-|`Total`|`number`|Full transaction total of receipt|$14.34|
-|`ArrivalDate`|`date`|Date of arrival|27Mar21|
-|`DepartureDate`|`date`|Date of departure|28Mar21|
-|`Currency`|`string`|Currency unit of receipt amounts (ISO 4217), or 'MIXED' if multiple values are found|USD|
-|`MerchantAliases`|`array`|||
-|`MerchantAliases.*`|`string`|Alternative name of merchant|Contoso (R)|
-|`Items`|`array`|||
-|`Items.*`|`object`|Extracted line item|1<br>Surface Pro 6<br>$999.00<br>$999.00|
-|`Items.*.TotalPrice`|`number`|Total price of line item|$999.00|
-|`Items.*.Description`|`string`|Item description|Room Charge|
-|`Items.*.Date`|`date`|Item date|27Mar21|
-|`Items.*.Category`|`string`|Item category|Room|
-
----
-
-::: moniker-end
-
 ::: moniker range="doc-intel-2.1.0"
 
 ### Migration guide and REST API v3.1

@@ -322,8 +322,28 @@ items:
       href: /python/api/overview/azure/ai-documentintelligence-readme?view=azure-python-preview&preserve-view=true
   - name: Schema
     items:
+    - name: Bank check
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/bank-check.md
+    - name: Bank statement
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/bank-statement.md
+    - name: Business card (2023-07-31 GA)
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2023-07-31/business-card.md
+    - name: Contract
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/contract.md
+    - name: Credit card
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/credit-card.md
+    - name: Health insurance card
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/health-insurance-card.md
+    - name: ID document
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/id-document.md
+    - name: Invoice
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/invoice.md
     - name: Marriage certificate
-      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/marriage-certificate/2024-07-31-preview.json?context=/azure/ai-services/context/context
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/marriage-certificate.md
+    - name: Pay stub
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/pay-stub.md
+    - name: Receipt
+      href: https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/schema/2024-07-31-preview/receipt.md
 - name: Resources
   items:
   - name: Enterprise readiness

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Evaluation of generative AI applications
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Advancements in language models such as GPT-4 via Azure OpenAI Service offer great promise while coming with challenges related to responsible AI. If not designed carefully, systems built upon these models can perpetuate existing societal biases, promote misinformation, create manipulative content, or lead to a wide range of other negative impacts. Addressing these risks while maximizing benefits to users is possible with an iterative approach through four stages: [identify, measure, and mitigate, operate](https://aka.ms/LLM-RAI-devstages).
 

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Content risk mitigation strategies with Azure AI
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Mitigating content risks and poor quality generations presented by large language models (LLMs) such as the Azure OpenAI models requires an iterative, layered approach that includes experimentation and continual measurement. We recommend developing a mitigation plan that encompasses four layers of mitigations for the identified risks in the earlier stages of the process:
 

@@ -17,7 +17,7 @@ author: lgayhardt
 
 # Evaluation and monitoring metrics for generative AI
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Azure AI Studio allows you to evaluate single-turn or complex, multi-turn conversations where you ground the generative AI model in your specific data (also known as Retrieval Augmented Generation or RAG). You can also evaluate general single-turn query and response scenarios, where no context is used to ground your generative AI model (non-RAG). Currently, we support built-in metrics for the following task types:
 

@@ -15,7 +15,7 @@ author: sdgilley
 
 # Fine-tune models in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Fine-tuning retrains an existing large language model (LLM) by using example data. The result is a new, custom LLM that's optimized for the provided examples.
 

@@ -15,7 +15,7 @@ author: lgayhardt
 
 # Transparency Note for Azure AI Studio safety evaluations
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 ## What is a Transparency Note
 

@@ -15,7 +15,7 @@ author: Blackmist
 
 # Vulnerability management for Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Vulnerability management involves detecting, assessing, mitigating, and reporting on any security vulnerabilities that exist in an organization's systems and software. Vulnerability management is a shared responsibility between you and Microsoft.
 

@@ -16,7 +16,7 @@ author: Blackmist
 
 # Autoscale Azure AI limits
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 This article provides guidance for how you can manage and increase quotas for resources with Azure AI Studio.
 

@@ -40,7 +40,7 @@ When you deploy a model from the model catalog (base or fine-tuned) by using ser
 
 The model processes your input prompts and generates outputs based on its functionality, as described in the model details. Your use of the model (along with the provider's accountability for the model and its outputs) is subject to the license terms for the model. Microsoft provides and manages the hosting infrastructure and API endpoint. The models hosted in this *model as a service* (MaaS) scenario are subject to Azure data, privacy, and security commitments. [Learn more about Azure compliance offerings applicable to Azure AI Studio](https://servicetrust.microsoft.com/DocumentPage/7adf2d9e-d7b5-4e71-bad8-713e6a183cf3).
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Microsoft acts as the data processor for prompts and outputs sent to, and generated by, a model deployed for pay-as-you-go inferencing (MaaS). Microsoft doesn't share these prompts and outputs with the model provider. Also, Microsoft doesn't use these prompts and outputs to train or improve Microsoft models, the model provider's models, or any third party's models.
 

@@ -17,7 +17,7 @@ author: Blackmist
 
 # How to add a new connection in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn how to add a new connection in Azure AI Studio.
 

@@ -16,7 +16,7 @@ author: Blackmist
 
 # Plan and manage costs for Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 This article describes how you plan for and manage costs for Azure AI Studio. First, you use the Azure pricing calculator to help plan for Azure AI Studio costs before you add any resources for the service to estimate costs. Next, as you add Azure resources, review the estimated costs.
 

@@ -15,7 +15,7 @@ author: Blackmist
 
 # Use an Azure Resource Manager template to create an Azure AI Studio hub
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Use a [Microsoft Bicep](/azure/azure-resource-manager/bicep/overview) template to create a hub for Azure AI Studio. A template makes it easy to create resources as a single, coordinated operation. A Bicep template is a text document that defines the resources that are needed for a deployment. It might also specify deployment parameters. Parameters are used to provide input values when using the template.
 

@@ -17,7 +17,7 @@ author: sdgilley
 
 # Create and manage prompt flow compute sessions in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 You need a compute session to run [prompt flows](prompt-flow.md). Use Azure AI Studio to create and manage prompt flow compute sessions.
 

@@ -16,7 +16,7 @@ author: sdgilley
 
 # How to create and manage compute instances in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn how to create a compute instance in Azure AI Studio. You can create a compute instance in the Azure AI Studio.
 

@@ -15,7 +15,7 @@ author: Blackmist
 
 # How to create a secure Azure AI Studio hub and project with a managed virtual network
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 You can secure your Azure AI Studio hub, projects, and managed resources in a managed virtual network. With a managed virtual network, inbound access is only allowed through a private endpoint for your hub. Outbound access can be configured to allow either all outbound access, or only allowed outbound that you specify. For more information, see [Managed virtual network](configure-managed-network.md).
 

@@ -15,7 +15,7 @@ author: fbsolo-ms1
 
 # How to add and manage data in your Azure AI Studio project
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 This article describes how to create and manage data in Azure AI Studio. Data can be used as a source for indexing in Azure AI Studio.
 

@@ -15,7 +15,7 @@ author: PatrickFarley
 
 # Azure OpenAI enterprise chat with images using GPT-4 Turbo with Vision
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Use this article to learn how to provide your own image data for GPT-4 Turbo with Vision, Azure OpenAI's vision model. GPT-4 Turbo with Vision enterprise chat allows the model to generate more customized and targeted answers using retrieval augmented generation based on your own images and image metadata.
 

@@ -16,12 +16,12 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Cohere Command chat models
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about Cohere Command chat models and how to use them.
 The Cohere family of models includes various models optimized for different use cases, including chat completions, embeddings, and rerank. Cohere models are optimized for various use cases that include reasoning, summarization, and question answering.
 
-
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -16,12 +16,12 @@ zone_pivot_groups: azure-ai-model-catalog-samples-embeddings
 
 # How to use Cohere Embed V3 models with Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about Cohere Embed V3 models and how to use them with Azure AI Studio.
 The Cohere family of models includes various models optimized for different use cases, including chat completions, embeddings, and rerank. Cohere models are optimized for various use cases that include reasoning, summarization, and question answering.
 
-
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -14,10 +14,12 @@ ms.custom: references_regions, build-2024
 
 # How to deploy Cohere Rerank models with Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about the Cohere Rerank models, how to use Azure AI Studio to deploy them as serverless APIs with pay-as-you-go token-based billing, and how to work with the deployed models.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
+
 ## Cohere Rerank models
 
 Cohere offers two Rerank models in [Azure AI Studio](https://ai.azure.com). These models are available in the model catalog for deployment as serverless APIs:

@@ -16,7 +16,7 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Jais chat models
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about Jais chat models and how to use them.
 JAIS 30b Chat is an autoregressive bi-lingual LLM for **Arabic** & **English**. The tuned versions use supervised fine-tuning (SFT). The model is fine-tuned with both Arabic and English prompt-response pairs. The fine-tuning datasets included a wide range of instructional data across various domains. The model covers a wide range of common tasks including question answering, code generation, and reasoning over textual content. To enhance performance in Arabic, the Core42 team developed an in-house Arabic dataset and translated some open-source English instructions into Arabic.
@@ -25,7 +25,7 @@ JAIS 30b Chat is an autoregressive bi-lingual LLM for **Arabic** & **English**.
 * **Input:** Model input is text only.
 * **Output:** Model generates text only.
 
-
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -14,14 +14,16 @@ ms.custom: references_regions
 
 # How to deploy AI21's Jamba family models with Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn how to use Azure AI Studio to deploy AI21's Jamba family models as a serverless API with pay-as-you-go billing.
 
 The Jamba family models are AI21's production-grade Mamba-based large language model (LLM) which leverages AI21's hybrid Mamba-Transformer architecture. It's an instruction-tuned version of AI21's hybrid structured state space model (SSM) transformer Jamba model. The Jamba family models are built for reliable commercial use with respect to quality and performance.
 
-> [!TIP]
-> See our announcements of AI21's Jamba family models available now on Azure AI Model Catalog through [AI21's blog](https://aka.ms/ai21-jamba-1.5-large-announcement) and [Microsoft Tech Community Blog](https://aka.ms/ai21-jamba-1.5-large-microsoft-annnouncement).
+See our announcements of AI21's Jamba family models available now on Azure AI Model Catalog through [AI21's blog](https://aka.ms/ai21-jamba-1.5-large-announcement) and [Microsoft Tech Community Blog](https://aka.ms/ai21-jamba-1.5-large-microsoft-annnouncement).
+
+[!INCLUDE [models-preview](../includes/models-preview.md)]
+
 
 ## Deploy the Jamba family models as a serverless API
 

@@ -16,12 +16,13 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use the Meta Llama family of models
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about the Meta Llama family of models and how to use them. Meta Llama models and tools are a collection of pretrained and fine-tuned generative AI text and image reasoning models - ranging in scale from SLMs (1B, 3B Base and Instruct models) for on-device and edge inferencing - to mid-size LLMs (7B, 8B and 70B Base and Instruct models) and high performant models like Meta Llama 3.1 405B Instruct for synthetic data generation and distillation use cases.
 
-> [!TIP]
-> See our announcements of Meta's Llama 3.2 family models available now on Azure AI Model Catalog through [Meta's blog](https://aka.ms/llama-3.2-meta-announcement) and [Microsoft Tech Community Blog](https://aka.ms/llama-3.2-microsoft-announcement).
+See our announcements of Meta's Llama 3.2 family models available now on Azure AI Model Catalog through [Meta's blog](https://aka.ms/llama-3.2-meta-announcement) and [Microsoft Tech Community Blog](https://aka.ms/llama-3.2-microsoft-announcement).
+
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -16,11 +16,12 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Mistral Nemo chat model
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about Mistral Nemo chat model and how to use them.
 Mistral AI offers two categories of models. Premium models including [Mistral Large and Mistral Small](deploy-models-mistral.md), available as serverless APIs with pay-as-you-go token-based billing. Open models including [Mistral Nemo](deploy-models-mistral-nemo.md), [Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01](deploy-models-mistral-open.md); available to also download and run on self-hosted managed endpoints.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -16,11 +16,12 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Mistral-7B and Mixtral chat models
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about Mistral-7B and Mixtral chat models and how to use them.
 Mistral AI offers two categories of models. Premium models including [Mistral Large and Mistral Small](deploy-models-mistral.md), available as serverless APIs with pay-as-you-go token-based billing. Open models including [Mistral Nemo](deploy-models-mistral-nemo.md), [Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01](deploy-models-mistral-open.md); available to also download and run on self-hosted managed endpoints.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -16,11 +16,12 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Mistral premium chat models
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about Mistral premium chat models and how to use them.
 Mistral AI offers two categories of models. Premium models including [Mistral Large and Mistral Small](deploy-models-mistral.md), available as serverless APIs with pay-as-you-go token-based billing. Open models including [Mistral Nemo](deploy-models-mistral-nemo.md), [Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01](deploy-models-mistral-open.md); available to also download and run on self-hosted managed endpoints.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -17,7 +17,7 @@ author: msakande
 
 # How to deploy Azure OpenAI models with Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn to create Azure OpenAI model deployments in Azure AI Studio.
 

@@ -16,12 +16,12 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Phi-3.5 chat model with vision
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about Phi-3.5 chat model with vision and how to use them.
 The Phi-3.5 small language models (SLMs) are a collection of instruction-tuned generative text models.
 
-
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -16,12 +16,12 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Phi-3 chat model with vision
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about Phi-3 chat model with vision and how to use them.
 The Phi-3 family of small language models (SLMs) is a collection of instruction-tuned generative text models.
 
-
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -16,12 +16,12 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Phi-3 family chat models
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn about Phi-3 family chat models and how to use them.
 The Phi-3 family of small language models (SLMs) is a collection of instruction-tuned generative text models.
 
-
+[!INCLUDE [models-preview](../includes/models-preview.md)]
 
 ::: zone pivot="programming-language-python"
 

@@ -20,6 +20,8 @@ ms.custom:
 
 In this article, you learn about which regions are available for each of the models supporting serverless API endpoint deployments.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
+
 Certain models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
 
 ## Region availability

@@ -19,6 +19,8 @@ ms.custom:
 
 In this article, you learn how to configure an existing serverless API endpoint in a different project or hub than the one that was used to create the deployment.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
+
 [Certain models in the model catalog](deploy-models-serverless-availability.md) can be deployed as serverless APIs. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
 
 The need to consume a serverless API endpoint in a different project or hub than the one that was used to create the deployment might arise in situations such as these:

@@ -17,6 +17,8 @@ ms.custom: build-2024, serverless, devx-track-azurecli
 
 In this article, you learn how to deploy a model from the model catalog as a serverless API with pay-as-you-go token based billing.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
+
 [Certain models in the model catalog](deploy-models-serverless-availability.md) can be deployed as a serverless API with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
 
 This article uses a Meta Llama model deployment for illustration. However, you can use the same steps to deploy any of the [models in the model catalog that are available for serverless API deployment](deploy-models-serverless-availability.md).

@@ -15,13 +15,15 @@ ms.custom: references_regions, build-2024
 
 # How to deploy a TimeGEN-1 model with Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn how to use Azure AI Studio to deploy the TimeGEN-1 model as a serverless API with pay-as-you-go billing.
 You filter on the Nixtla collection to browse the TimeGEN-1 model in the [Model Catalog](model-catalog.md).
 
 The Nixtla TimeGEN-1 is a generative, pretrained forecasting and anomaly detection model for time series data. TimeGEN-1 can produce accurate forecasts for new time series without training, using only historical values and exogenous covariates as inputs.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
+
 ## Deploy TimeGEN-1 as a serverless API
 
 Certain models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.

@@ -13,7 +13,7 @@ author: sdgilley
 
 # How to get started with an AI template
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 Streamline your code-first development with prebuilt, task-specific Azure AI templates. Benefit from using the latest features and best practices from Microsoft Azure AI, with popular frameworks like LangChain, prompt flow, and Semantic Kernel in multiple languages.
 

@@ -15,7 +15,7 @@ author: Blackmist
 
 # Add a new connection using the Azure Machine Learning SDK
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 In this article, you learn how to add a new connection using the Azure Machine Learning SDK.
 

@@ -14,7 +14,7 @@ author: sdgilley
 
 # Create a hub using the Azure Machine Learning SDK and CLI
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 In this article, you learn how to create the following AI Studio resources using the Azure Machine Learning SDK and Azure CLI (with machine learning extension):
 - An Azure AI Studio hub

@@ -15,7 +15,7 @@ author: lgayhardt
 ---
 # Evaluate with the Azure AI Evaluation SDK
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 > [!NOTE]
 > Evaluate with the prompt flow has been retired and replaced with Azure AI Evaluate.

@@ -15,7 +15,7 @@ author: sdgilley
 
 # How to build and consume an index using code
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 In this article, you learn how to create an index and consume it from code. To create an index locally, we use the `promptflow-rag` package. To create a remote index on the cloud, we use the `azure-ai-ml` package. We consume the indexes using `langchain`.
 
@@ -183,7 +183,7 @@ client.indexes.create_or_update(
 ```
 
 > [!NOTE]
-> Environment variables are intended for convenience in a local environment. However, if you register a local index created using environment variables, the index may not function as expected because secrets from environment variables won’t be transferred to the cloud index. To address this issue, you can use a `ConnectionConfig` or `connection_id` to create a local index before registering.
+> Environment variables are intended for convenience in a local environment. However, if you register a local index created using environment variables, the index may not function as expected because secrets from environment variables won't be transferred to the cloud index. To address this issue, you can use a `ConnectionConfig` or `connection_id` to create a local index before registering.
 
 ## Build an index (remotely) in your AI Studio project
 

@@ -17,7 +17,7 @@ author: lgayhardt
 
 # Generate synthetic and simulated data for evaluation
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 Large language models are known for their few-shot and zero-shot learning abilities, allowing them to function with minimal data. However, this limited data availability impedes thorough evaluation and optimization when you might not have test datasets to evaluate the quality and effectiveness of your generative AI application. 
 

@@ -15,7 +15,7 @@ author: sdgilley
 
 # How to trace your application with prompt flow SDK | Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 Tracing is a powerful tool that offers developers an in-depth understanding of the execution process of their generative AI applications such as agents, [AutoGen](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat), and retrieval augmented generation (RAG) use cases. It provides a detailed view of the execution flow, including the inputs and outputs of each node within the application. This essential information proves critical while debugging complex applications or optimizing performance.
 

@@ -15,7 +15,7 @@ author: lgayhardt
 
 # Enable tracing and collect feedback for a flow deployment
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 After deploying a Generative AI APP in production, you might want to enhance your understanding and optimize performance. Trace data for each request, aggregated metrics, and user feedback play critical roles.
 

@@ -17,7 +17,7 @@ author: sdgilley
 
 # Get started with Azure AI Studio projects in VS Code (Preview)
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 Azure AI Studio supports developing in VS Code - Desktop and Web. In each scenario, your VS Code instance is remotely connected to a prebuilt custom container running on a virtual machine, also known as a compute instance.
 

@@ -15,7 +15,7 @@ ms.date: 5/21/2024
 
 # Customer enabled disaster recovery
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 To maximize your uptime, plan ahead to maintain business continuity and prepare for disaster recovery with Azure AI Studio. Since Azure AI Studio builds on [Azure Machine Learning architecture](/azure/machine-learning/concept-workspace), it's beneficial to reference the foundational architecture.
 

@@ -14,7 +14,7 @@ author: lgayhardt
 
 # How to evaluate generative AI apps with Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 To thoroughly assess the performance of your generative AI application when applied to a substantial dataset, you can initiate an evaluation process. During this evaluation, your application is tested with the given dataset, and its performance will be quantitatively measured with both mathematical based metrics and AI-assisted metrics. This evaluation run provides you with comprehensive insights into the application's capabilities and limitations. 
 

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Manually evaluate prompts in Azure AI Studio playground
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 When you get started with prompt engineering, you should test different inputs one at a time to evaluate the effectiveness of the prompt can be very time intensive. This is because it's important to check whether the content filters are working appropriately, whether the response is accurate, and more. 
 

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # How to view evaluation results in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 The Azure AI Studio evaluation page is a versatile hub that not only allows you to visualize and assess your results but also serves as a control center for optimizing, troubleshooting, and selecting the ideal AI model for your deployment needs. It's a one-stop solution for data-driven decision-making and performance enhancement in your AI Studio projects. You can seamlessly access and interpret the results from various sources, including your flow, the playground quick test session, evaluation submission UI, and SDK. This flexibility ensures that you can interact with your results in a way that best suits your workflow and preferences.
 

@@ -15,7 +15,7 @@ ms.custom: references_regions, build-2024
 
 # Fine-tune Meta Llama models in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Azure AI Studio lets you tailor large language models to your personal datasets by using a process known as *fine-tuning*. 
 
@@ -25,6 +25,8 @@ In this article, you learn how to fine-tune Meta Llama models in [Azure AI Studi
 
 The [Meta Llama family of large language models (LLMs)](./deploy-models-llama.md) is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. The model family also includes fine-tuned versions optimized for dialogue use cases with Reinforcement Learning from Human Feedback (RLHF), called Llama-Instruct.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
+
 ## Models
 # [Meta Llama 3.1](#tab/llama-three)
 

@@ -8,14 +8,19 @@ ms.custom:
 ms.topic: how-to
 ms.date: 7/16/2024
 ---
+
 # Fine-tune Phi-3 models in Azure AI Studio
 
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
+
 Azure AI Studio lets you tailor large language models to your personal datasets by using a process known as fine-tuning. Fine-tuning provides significant value by enabling customization and optimization for specific tasks and applications. It leads to improved performance, cost efficiency, reduced latency, and tailored outputs.
 
 In this article, you learn how to fine-tune Phi-3 family of small language models (SLMs) in Azure AI Studio as a service with pay-as you go billing.
 
 The Phi-3 family of SLMs is a collection of instruction-tuned generative text models.  Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across various language, reasoning, coding, and math benchmarks.
 
+[!INCLUDE [models-preview](../includes/models-preview.md)]
+
 ## [Phi-3-mini](#tab/phi-3-mini)
 
 Phi-3 Mini is a 3.8B parameters, lightweight, state-of-the-art open model built upon datasets used for Phi-2 - synthetic data and filtered websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-3 model family, and the Mini version comes in two variants 4K and 128K which is the context length (in tokens) it can support.

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Submit a batch run and evaluate a flow
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 To evaluate how well your flow performs with a large dataset, you can submit batch run and use an evaluation method in prompt flow.
 

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Deploy a flow for real-time inference
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 After you build a prompt flow and test it properly, you might want to deploy it as an online endpoint. Deployments are hosted within an endpoint, and can receive data from clients and send responses back in real-time.
 

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Develop an evaluation flow in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Evaluation flows are special types of flows that assess how well the outputs of a run align with specific criteria and goals.
 

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Develop a prompt flow
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Prompt flow is a development tool designed to streamline the entire development cycle of AI applications powered by Large Language Models (LLMs). Prompt flow provides a comprehensive solution that simplifies the process of prototyping, experimenting, iterating, and deploying your AI applications.
 

@@ -15,7 +15,7 @@ author: lgayhardt
 
 # Process images in prompt flow
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Multimodal Large Language Models (LLMs), which can process and interpret diverse forms of data inputs, present a powerful tool that can elevate the capabilities of language-only systems to new heights. Among the various data types, images are important for many real-world applications. The incorporation of image data into AI systems provides an essential layer of visual understanding. 
 

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Tune prompts using variants in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn how to use variants to tune prompts and evaluate the performance of different variants.
 

@@ -13,7 +13,7 @@ ms.author: pafarley
 
 # Groundedness detection (preview)
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Learn how to use Azure AI Content Safety Groundedness detection to check whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users.
 

@@ -16,7 +16,7 @@ author: ssalgadodev
 
 # How to build and consume vector indexes in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn how to create and use a vector index for performing [Retrieval Augmented Generation (RAG)](../concepts/retrieval-augmented-generation.md).
 

@@ -16,7 +16,7 @@ author: sdgilley
 
 # Model benchmarks in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In Azure AI Studio, you can compare benchmarks across models and datasets available in the industry to decide which one meets your business scenario. You can find model benchmarks under **Get started** on the left menu in Azure AI Studio.
 

@@ -17,13 +17,13 @@ author: ssalgadodev
 
 # Model catalog and collections in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 The model catalog in Azure AI Studio is the hub to discover and use a wide range of models for building generative AI applications. The model catalog features hundreds of models across model providers such as Azure OpenAI Service, Mistral, Meta, Cohere, NVIDIA, and Hugging Face, including models that Microsoft trained. Models from providers other than Microsoft are Non-Microsoft Products as defined in [Microsoft Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage) and are subject to the terms provided with the models.
 
 ## Model collections
 
-The model catalog organizes models into three types collections:
+The model catalog organizes models into three collections:
 
 * **Curated by Azure AI**: The most popular non-Microsoft open-weight and proprietary models packaged and optimized to work seamlessly on the Azure AI platform. Use of these models is subject to the model providers' license terms. When you deploy these models in Azure AI Studio, their availability is subject to the applicable [Azure service-level agreement (SLA)](https://www.microsoft.com/licensing/docs/view/Service-Level-Agreements-SLA-for-Online-Services), and Microsoft provides support for deployment problems.
 

@@ -17,7 +17,7 @@ author: msakande
 
 # Monitor quality and token usage of deployed prompt flow applications
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 Monitoring applications that are deployed to production is an essential part of the generative AI application lifecycle. Changes in data and consumer behavior can influence your application over time, resulting in outdated systems that negatively affect business outcomes and expose organizations to compliance, economic, and reputation risks. 
 

@@ -15,7 +15,7 @@ author: lgayhardt
 
 # Azure OpenAI GPT-4 Turbo with Vision tool in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 The prompt flow Azure OpenAI GPT-4 Turbo with Vision tool enables you to use your Azure OpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them.
 

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Content safety tool for flows in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 The prompt flow Content Safety tool enables you to use Azure AI Content Safety in Azure AI Studio.
 
@@ -26,7 +26,7 @@ Azure AI Content Safety is a content moderation service that helps detect harmfu
 
 To create an Azure Content Safety connection:
 
-1. Sign in to [Azure AI Studio](https://studio.azureml.net/).
+1. Sign in to [Azure AI Studio](https://ai.azure.com/).
 1. Go to **Project settings** > **Connections**.
 1. Select **+ New connection**.
 1. Complete all steps in the **Create a new connection** dialog. You can use an Azure AI Studio hub or Azure AI Content Safety resource. We recommend that you use a hub that supports multiple Azure AI services.

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # Embedding tool for flows in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 The prompt flow Embedding tool enables you to convert text into dense vector representations for various natural language processing tasks.
 

@@ -15,7 +15,7 @@ author: lgayhardt
 
 # Index Lookup tool for Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 The prompt flow Index Lookup tool enables the use of common vector indices (such as Azure AI Search, Faiss, and Pinecone) for retrieval augmented generation in prompt flow. The tool automatically detects the indices in the workspace and allows the selection of the index to be used in the flow.
 

@@ -16,7 +16,7 @@ author: lgayhardt
 
 # LLM tool for flows in Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
 To use large language models (LLMs) for natural language processing, you use the prompt flow LLM tool.