A systems administrator is tasked with parsing apache access logs to identify all requests that resulted in a 404 Not Found error. To speed up the process, the administrator prompts a public AI chatbot: "Generate a grep command with a regular expression to find all lines in an apache access log containing a 404 status code." The AI returns a command, which the administrator immediately incorporates into a script that runs on a production web server. The script later causes unexpected behavior by incorrectly filtering log entries. Which AI best practice was most critical for the administrator to have followed to prevent this issue?
Engaging in more detailed prompt engineering to include the specific log format.
Ensuring the AI model was specifically trained on system administration tasks.
Verifying the output of the AI-generated regular expression in a test environment.
Using a local, privately-hosted AI model instead of a public one.
The correct answer is to verify the output of the AI-generated regular expression in a test environment. A core principle of responsibly using AI for code or command generation is to never blindly trust the output. The administrator is ultimately responsible for any code they implement, regardless of its origin. Before deploying the AI-generated command into a production script, it should have been thoroughly tested on sample log data to ensure it behaved as expected and did not produce false positives or negatives. While better prompt engineering could improve the result, and using a local model could be better for data privacy, neither of these actions replaces the fundamental need to test and validate the output before deployment.
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What does the `grep` command do in Linux?
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