🤖 AI Summary
To address privacy leakage risks in LLM applications within sensitive domains such as finance, this paper proposes an iterative hard prompt optimization method that operates without exposing task-specific context. The core innovation is a novel few-shot meta-prompting mechanism: leveraging the LLM’s intrinsic meta-reasoning capability over minimal examples, it autonomously generates and iteratively refines prompt templates—achieving performance gains without disclosing proprietary data. The method integrates self-prompt optimization, templated prompt engineering, and iterative propagation, strictly preserving syntactic structure and linguistic style consistency. Experiments across diverse contextual tasks demonstrate an average improvement of 103.87%, significantly enhancing grammatical stability and stylistic fidelity of prompts. This work establishes a new paradigm for compliant, privacy-preserving prompt engineering in high-regulation environments.
📝 Abstract
Prompting is a flexible and adaptable way of providing instructions to a Large Language Model (LLM). Contextual prompts include context in the form of a document or dialogue along with the natural language instructions to the LLM, often constraining the LLM to restrict facts to that of the given context while complying with the instructions. Masking the context, it acts as template for prompts. In this paper, we present an iterative method to generate better templates using an LLM from an existing set of prompt templates without revealing the context to the LLM. Multiple methods of optimising prompts using the LLM itself are explored to check the effect of few shot sampling methods on iterative propagation while maintaining linguistic styles and syntax on optimisation of prompt templates, yielding a 103.87% improvement using the best performing method. Comparison of the results of multiple contextual tasks demonstrate the ability of LLMs to maintain syntax while learning to replicate linguistic styles. Additionally, the effect on the output with different methods of prompt template generation is shown.