When a Schematron rule fails during validation, it generates an error message indicating the context of the error. However, it does not provide a fix for the issue. The fix for the error message can be generated using AI, which analyzes the context of the rule and suggests a correction to the XML document. This can help to automate the process of fixing errors in XML documents and save time for developers.

<sch:rule context="shortdesc">
    <sch:report test="count(tokenize(.,'\s+')) > 50">
         The description must contain less than 50 words.</sch:report>
</sch:rule>

You can use the Schematron rule to generate a fix using AI. The content to correct is provided in the context of the Schematron rule, and the goal is to use AI to generate a new text that will serve as a fix for the issue. Specifically, the AI should generate a new text to replace the content of the "shortdesc" element, which should contain a brief description of the issue.

This approach can be extended beyond Schematron to other types of error messages in software development. By using AI to generate fixes automatically, developers can save time and focus on more complex tasks, while also improving the overall quality of their code.

Of course, there are some challenges to this approach. For example, the message needs to be as concise as possible so that the AI can use it effectively. Additionally, sometimes the context of the message may need to be modified or additional operations may need to be performed to generate an effective fix. However, overall this approach can save a lot of time and effort compared to manually creating