Anyone know prior research on rewriting a rough prompt per model?

A developer is exploring the idea of using an LLM to rewrite rough, half-formed prompts into optimized prompts tailored for specific models like Claude, GPT, and Gemini. They are seeking prior research, papers, or repositories on model-specific prompt adaptation.

I've been kicking around an idea and want to read up before I build it. The idea: you type a rough, half-formed prompt, an LLM works out what you actually mean, and then rewrites it into a specific, optimized prompt tuned for each target model Claude, GPT, Gemini, etc. . Before I start building, I'd love to find prior work. Specifically, I'm looking for: Is this already well-studied and I'm just searching the wrong terms? If you've run across papers, GitHub repositories, or blog posts tackling model-specific prompt adaptation, please drop them in the comments below What keywords should I actually be searching for?