This paper explores the potential of Large Language Models (LLMs) to enhance community detection algorithms, with a focus on the SIWO (Strong In, Weak Out) algorithm. By integrating LLMs into the algorithm development process, focusing on their multi-disciplinary knowledge as a potential advantage over human expertise, we demonstrate how LLMs (with the possible oversight of a human expert) can generate innovative algorithm modifications that lead to enhanced performance. Our study reveals substantial reductions in execution times by more than 50% for SIWO when utilizing these modifications. Motivated by these promising results within the domain of Social Networks Analysis, we briefly introduce the Algorithmic Enhancement Framework (AEF), designed to extend these methodologies for broader algorithm enhancement. AEF employs the collaborative use of LLMs to generate and refine solutions iteratively, offering a novel foundational approach for incorporating LLM capabilities for the refinement of algorithms across a broad range of computational domains.