A perspective which has helped me is viewing LLM-based offerings strictly as statistical document generators, whose usefulness is entirely dependent upon their training data set plus model evolution, and whose usage is best modeled as a form of constraint programming[0] lacking a formal (repeatable) grammar. As such, and when considering the subjectivity of natural languages in general, the best I hope for when using them are quick iterations consisting of refining constraint sentence fragments.

Here is a simple example which took 4 iterations using Gemini to get a result requiring no manual changes:

  # Role
  You are an expert Unix shell programmer who comments their code and organizes their code using shell programming best practices.

  Create a bash shell script which reads from standard input text in Markdown format and prints all embedded hyperlink URL's.

  The script requirements are:

    - MUST exclude all inline code elements
    - MUST exclude all fenced code blocks
    - MUST print all hyperlink URL's
    - MUST NOT print hyperlink label
    - MUST NOT use Perl compatible regular expressions
    - MUST NOT use double quotes within comments
    - MUST NOT use single quotes within comments

EDIT:

For reference, a hand-written script satisfying the above (excluding comments for brevity) could look like:

  #!/usr/bin/env bash

  perl -ne 'print unless /^```/ ... /^```/' |
      sed -e 's/`[^`]*`//g' |
      egrep -o '\[.+?\]\(.+?\)' |
      sed -e 's/^.*(//' -e 's/)$//'

0 - https://en.wikipedia.org/wiki/Constraint_programming