AI code refactoring improves code structure, readability, and maintainability while preserving functionality. Refactoring types: Rename variables for clarity, extract functions/methods, remove duplication, simplify conditionals, improve abstractions, update to modern syntax, apply design patterns. LLM capabilities: Understand intent behind code, suggest structural improvements, implement refactoring transformations, explain changes. Traditional tools: IDE refactoring (rename, extract, inline), linters with auto-fix, formatters. AI-enhanced refactoring: Holistic improvements considering context, natural language instructions (make this more readable), complex multi-file restructuring. Prompt patterns: Refactor this code to be more readable, Extract reusable functions, Apply specific pattern to this code, Modernize this code. Quality considerations: Preserve behavior (critical!), maintain or improve performance, follow codebase conventions. Testing importance: Comprehensive test suite before refactoring, verify tests pass after. Use cases: Technical debt reduction, code review feedback implementation, legacy code modernization. AI accelerates refactoring but verification remains essential.