Writing clean, maintainable code is a constant struggle for developers. Long, complex methods are a common culprit, making code hard to understand, debug, and modify. The solution? A refactoring technique called "Extract Method," which breaks down these unwieldy methods into smaller, more manageable chunks. While modern IDEs support this, knowing *what* to extract is the tricky part. Existing tools often miss the mark, proposing refactorings that don't align with real-world developer practices. But what if AI could lend a hand? Researchers have developed EM-Assist, a new IntelliJ IDEA plugin that uses Large Language Models (LLMs) to suggest intelligent Extract Method refactorings. EM-Assist analyzes your code, proposes refactorings that mirror how human developers think, and then uses the IDE's power to safely apply the changes. Unlike previous tools that rely on rigid rules, EM-Assist learns from massive codebases, generating suggestions that are more likely to be accepted by developers. In tests replicating real-world refactorings, EM-Assist significantly outperformed existing tools, boasting a 53.4% recall rate compared to 39.4% for the previous best. A survey of professional developers also confirmed its usability, with a 94.4% positive rating. This points to a future where AI seamlessly integrates into our coding workflows, helping us write cleaner, more efficient code. While challenges remain, such as handling proprietary code and improving response times, EM-Assist represents a significant step towards AI-assisted refactoring. As LLMs continue to evolve, we can expect even more sophisticated tools that empower developers to write better code with less effort.