Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
This excerpt introduces meta-prompting, a novel scaffolding technique to enhance language models by enabling them to function as both orchestrators and specialists. It leverages high-level directives for decomposing complex tasks into simpler subtasks, tackled by expert instances of the same model under specific instructions. This method transforms a single language model into a multi-functional entity, capable of conducting integrated, expert-level analyses and generating refined outcomes. Meta-prompting’s task-agnostic framework simplifies user interactions and incorporates external tools like Python interpreters, significantly improving task performance. Research with GPT-4 demonstrates its effectiveness, showing a marked performance improvement over traditional prompting methods.