> This is an area I'm very interested in. Do you have a particular application in mind? (I'm guessing the recipe example is just illustrate the general principle.)

You are right in identifying the recipe example as being illustrative and intentionally simple. A more realistic example of using constraint programming techniques with LLMs is:

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

  # Task
  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
  

Where this approach originates is in attempting to limit LLM token generation variance by minimizing use of English vocabulary and sentence structure expressivity such that document generation has a higher probability of being repeatable. The epiphany I experienced was that by interacting with LLMs as a "black box" whose results can only be influenced, and not anthropomorphizing them, the natural way to do so is to leverage their NLP capabilities to produce restrictions (search tree pruning) for a declarative query (initial search space).