# Meta Prompting for AGI Systems

**Authors:** Yifan Zhang  
**arXiv:** [2311.11482](https://arxiv.org/abs/2311.11482)  
**Date:** 2023-11-20  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.11482v1/

## Key points

- Meta Prompting is presented as a novel technique that transforms problem-solving and data interpretation approaches of large language models, multi-modal foundation models, and AI systems.
- Rooted in type theory and category theory, Meta Prompting prioritizes the structure and syntax of information, offering a unique framework beyond traditional content-focused methods.
- The paper contrasts Meta Prompting with Few-Shot Prompting, showcasing its applicability and superiority across various AI applications.
- Meta Prompting effectively breaks down intricate problems into manageable sub-problems, enabling a detailed step-by-step approach to problem-solving with token efficiency.
- The technique extends into complex reasoning tasks and multi-modal foundation model settings by integrating diverse data types such as images, audio, and video within its structured framework.
- This extension highlights the challenges and vast potential of using Meta Prompting for handling complex, multi-faceted data formats.
- Overall, "Meta Prompting for AGI Systems" revolutionizes problem-solving strategies in AI systems by emphasizing information structure and syntax while demonstrating effectiveness in complex reasoning tasks and multi-modal data integration scenarios.

## Summary

In the paper "Meta Prompting for AGI Systems" by Yifan Zhang, an in-depth exploration of Meta Prompting is presented as a novel technique that transforms the problem-solving and data interpretation approaches of large language models (LLMs), multi-modal foundation models, and AI systems. Rooted in type theory and category theory, Meta Prompting prioritizes the structure and syntax of information, offering a unique framework that goes beyond traditional content-focused methods. The formal definitions of Meta Prompting are discussed, contrasting it with Few-Shot Prompting and showcasing its applicability and superiority across various AI applications. A key focus of the paper is on expanding Meta Prompting into complex reasoning tasks. The technique is shown to effectively break down intricate problems into manageable sub-problems, enabling a detailed step-by-step approach to problem-solving. This method stands out for its token efficiency and ability to provide a fair comparison in problem-solving scenarios compared to few-shot example approaches. Moreover, the paper extends the application of Meta Prompting into multi-modal foundation model settings. By integrating diverse data types such as images, audio, and video within the structured framework of Meta Prompting, the challenges and vast potential of this approach in handling complex, multi-faceted data are highlighted. This extension marks new ground in leveraging Meta Prompting for handling a wide range of data formats. Overall, "Meta Prompting for AGI Systems" offers a comprehensive exploration of how Meta Prompting revolutionizes problem-solving strategies in AI systems by emphasizing information structure and syntax while demonstrating its effectiveness in complex reasoning tasks and multi-modal data integration scenarios. The code for implementing Meta Prompting is available at https://github.com/meta-prompting/meta-prompting.