Linear Feedback Control Systems for Iterative Prompt Optimization in Large Language Models

TL;DR

This paper introduces a novel method for iterative prompt optimization in large language models by applying principles from linear feedback control systems to improve output accuracy through systematic prompt refinement.

Contribution

It presents a new framework that models prompt refinement as a feedback control problem, integrating linear control principles with the non-linear behavior of LLMs for improved output quality.

Findings

Framework effectively guides prompt refinement process

Mathematical foundation for control-based prompt optimization

Potential for systematic and automated prompt tuning

Abstract

Large Language Models (LLMs) have revolutionized various applications by generating outputs based on given prompts. However, achieving the desired output requires iterative prompt refinement. This paper presents a novel approach that draws parallels between the iterative prompt optimization process in LLMs and feedback control systems. We iteratively refine the prompt by treating the deviation between the LLM output and the desired result as an error term until the output criteria are met. This process is akin to a feedback control system, where the LLM, despite being non-linear and non-deterministic, is managed using principles from linear feedback control systems. We explore the application of different types of controllers within this framework, providing a mathematical foundation for integrating linear feedback control mechanisms with LLMs.