Vibe coding can assist with refactoring large legacy codebases, but it is not a drop-in solution for the entire process. It performs best when developers guide it through controlled, incremental improvements rather than asking it to rewrite huge sections at once. Legacy code often contains implicit rules, hidden constraints, and historical quirks that the model cannot infer on its own. When developers provide specific files or functions and request targeted improvements—such as rewriting a function for readability or splitting a monolithic module into smaller parts—the model can produce helpful, clean alternatives that maintain the original behavior.
A practical workflow is to identify isolated sections of the legacy system and gradually modernize them using the model's assistance. For example, if your codebase predates vector search features and you want to integrate Milvus, you can use vibe coding to scaffold new modules rather than modifying the old ones directly. The model can help generate a modern ingestion pipeline, updated API endpoints, and structured utility functions that blend with your existing architecture. It can also help translate old procedural logic into more modular patterns, making future maintenance easier.
Where vibe coding struggles is in understanding cross-module dependencies in deeply intertwined systems. Legacy codebases often lack documentation and contain irregular naming conventions that confuse automated generation. Developers must provide summaries, dependency maps, or specific constraints to keep the model aligned. The best use of vibe coding in refactoring is as an assistant—not a full automation tool. It speeds up rewriting targeted sections, modernizing patterns, and generating clean replacements, but final integration and correctness remain responsibilities of the engineering team.