How to Use LLMs for Continuous, Creative Code Refactoring (thenewstack.io)

Recent advancements in AI-assisted coding tools, specifically those utilizing Large Language Models (LLMs) with Model Context Protocol (MCP) support, have transformed the code refactoring process from manual and limited transforms to a continuous, creative approach. Unlike traditional Integrated Development Environments (IDEs) that only offer predefined refactoring options, LLMs can learn from specified patterns and apply various transforms, simplifying complex tasks like extracting components and reducing repetition. For example, LLMs such as Claude, Cursor, and Codex have demonstrated the ability to propose meaningful changes to XMLUI applications by identifying patterns and suggesting improvements without needing explicit rule support. This evolution in coding practices holds significant implications for the AI/ML community, emphasizing a shift towards leveraging AI for more effective and meticulous coding rather than merely generating code. Though AI-driven tools can increase software liability when misused, they also present opportunities to streamline and maintain cleaner codebases through continuous refactoring. By adopting a collaborative, iterative approach with LLMs, developers can adapt their plans dynamically and consolidate common functionalities, ultimately leading to a "less is more" philosophy in coding—reducing unnecessary complexity while using AI to facilitate and validate creative coding insights.