This is the complete opposite of how LLMs are trained. LLMs are most effectively prompted (for instruct/chat finetunes anyway, i.e. chatbots) through the same kind of language patterns (natural or formal/programming) that they learn from. Trying to write formal prompts to them is exactly as misguided as speaking to your friends and family in C.
This. This is the most important thing to consider: the available corpus the model was trained on. Remember that LLMs are inferring code. They don't "know" anything at all about its axiomatic workings. They just know what "looks right" and what "looks wrong". Agentic and RL are about to make this philosophy obsolete on grand scale, but signs still don't look good for being any to improve how much they can "hold in their head" to infer what token to spit out next from the vector embedding, tho.
The language using the fewest punctuation tokens is going to be the safest from most categories of hallucination, and give each context window the greatest usable space for vector manipulation headed into self-attention before the model suffers from "vector-clouded judgment" due to overcrowded latent space.