A Linux administrator is tasked with updating a series of complex, legacy shell scripts that have no existing documentation. To accelerate the process, the administrator uses a large language model (LLM) to generate comments and a summary for each script. According to responsible AI best practices, what is the most appropriate next step for the administrator to take?
Copy and paste the generated documentation directly into the scripts to save time and immediately commit the changes to the central repository.
Thoroughly review the AI-generated documentation for technical accuracy and contextual correctness, then manually integrate it into the scripts or a separate documentation file.
Trust the AI's output as sufficient and archive the generated documentation in a shared folder without adding it to the scripts.
Discard the generated documentation and use the AI to refactor the entire script into a more modern, self-documenting language like Python.
The correct answer is to thoroughly review the AI-generated output for accuracy and context before integrating it. AI tools can generate plausible-sounding but incorrect or incomplete documentation. Best practices for responsible AI usage mandate that a human expert must verify the output, especially for technical and business-logic-specific details, to ensure it is accurate and relevant. Simply copying the output without review is irresponsible and can lead to propagating errors. While AI can be used for refactoring or generating tests, those are different tasks and do not fulfill the primary requirement of documenting the existing scripts. Generating documentation and then immediately archiving it without integrating it into the codebase or a version control system provides no value to future developers.
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