We introduce meta-prompting, an effective scaffolding technique designed to enhance the
functionality of language models (LMs). This approach transforms a single LM into a multi-
faceted conductor, adept at managing and integrating multiple independent LM queries. By
employing high-level instructions, meta-prompting guides the LM to break down complex
tasks into smaller, more manageable subtasks. These subtasks are then handled by distinct"
expert" instances of the same LM, each operating under specific, tailored instructions …