> For the complete documentation index, see [llms.txt](https://documentation.astera.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.astera.com/astera-data-stack-v10/report-model/ai-powered-data-extraction/best-practices-for-ai-powered-template-creation-in-astera.md).

# Best Practices for AI-Powered Template Creation in Astera

The latest enhancement in Astera leverages artificial intelligence (AI) to suggest report model templates. This innovation empowers you to effortlessly create models for numerous source files simultaneously. Simply by defining the layout and document type, Astera intelligently recommends the most appropriate model templates, significantly reducing the time and effort required to construct your data extraction processes. This advanced functionality allows you to optimize your workflow and eliminate the manual extraction of data, resulting in increased efficiency.

This document provides a set of guidelines recommended to be followed to achieve the best results in data extraction using AI.

## Best Practices:

1\. It is recommended to utilize this feature for documents spanning only one page. For larger documents, it would be quite time consuming and resource intensive in terms of processing.

2\. Correctly select the document type, purchase order or invoice, in the Report Model Schema Wizard.

3\. Provide a clear Data Layout based on the standards in the next point. The layout must contain appropriate regions and data fields named correctly. This will ensure accuracy in the generated templates.

4\. Ensure that the documents that are being used have a layout where the first data region has key-value pairs followed by a table and the last region being a key-value pair region as well. For example, the purchase order shown below follows such a layout.

![](/files/lLa8yI8POFZneAv84cAL)

Invoices could also be documents that follow such a layout.

5\. After template creation, thoroughly review and verify the generated templates for accuracy. Identify any missing fields or errors and adjust as needed. Once generated and in use, regularly validate and update the templates to reflect any changes in newer document layouts or data requirements.

6\. Validate Extracted Data: Always validate the accuracy and completeness of the extracted data against the original documents. Perform regular quality checks to ensure the extracted information aligns with the intended data fields and meets your business requirements.

By following these best practices, you can maximize the effectiveness of AI-powered template creation in Astera and achieve accurate and efficient data extraction from diverse document layouts while leveraging the capabilities of ChatGPT for improved results.


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