I don't understand how this could ever solve the underlying weaknesses in an LLM algorithm which is make_bigger(a_document) .

It's really a prompt engineering thing. Prompt engineering allows you to play off different areas of the LLM's training data against each other to produce a useful result by returning the intersection between the two sets (knowledge areas) of training data. The make_bigger(document) effect occurs when you let the LLM freewheel.

I wrote this language to address the issue with letting AI into production environments, which is that the AI is unpredictable to a certain degree. The purpose of this language is to provide hard, defined constraints in which the AI's human-like processing is invoked. All feedback welcome! Thanks, Edward