  Despite the popularity of Large Language Models (LLMs), crafting specific
prompts for LLMs to perform particular tasks remains challenging. Users often
engage in multiple conversational turns with an LLM-based agent to accomplish
their intended task. Recent studies have demonstrated that linguistic feedback,
in the form of self-reflections generated by the model, can work as
reinforcement during these conversations, thus enabling quicker convergence to
the desired outcome. Motivated by these findings, we introduce METAREFLECTION,
a novel technique that learns general prompt instructions for a specific domain
from individual self-reflections gathered during a training phase. We evaluate
our technique in two domains: Infrastructure as Code (IAC) vulnerability
detection and question-answering (QA) using REACT and COT. Our results
demonstrate a notable improvement, with METARELECTION outperforming GPT-4 by
16.82% (IAC), 31.33% (COT), and 15.42% (REACT), underscoring the potential of
METAREFLECTION as a viable method for enhancing the efficiency of LLMs.
