Vibe coding handles complex multi-file application structures by generating code in modular segments rather than trying to produce an entire system in one step. The model performs best when developers explicitly define the structure they want—such as directories for routes, services, database drivers, and utilities—and then request each file or component separately. For example, a developer might first instruct the model to create a /db/milvus_client.py module, then a /services/search_service.py module that imports the Milvus client, and finally an API route file that calls the service. Breaking the task into steps gives the model enough context to maintain consistent imports and naming conventions across files.
Another way vibe coding supports multi-file systems is through iterative refinement. Developers can share portions of the codebase—like directory listings, class definitions, or method signatures—so the model can reference existing code when generating new pieces. This prevents mismatches in function names, duplicate logic, or missing dependencies. If changes need to be made across several files, developers often paste the relevant segments and request synchronized updates. This mirrors how a human teammate would interact: by looking at what already exists and extending it carefully. In vector database scenarios, this step-by-step approach is particularly valuable because ingestion workflows, index-building functions, and search APIs usually live in separate modules.
That said, developers still play a key role in maintaining overall structure. Vibe coding will not automatically detect circular imports, enforce architecture rules, or reorganize directories unless explicitly instructed. It’s common to periodically ask the model to “review these modules for inconsistencies” or “generate a summary of the codebase for validation.” This makes vibe coding most effective as a code generator within a developer-directed architecture. When used this way, it handles complex, multi-file applications without losing coherence, especially in projects involving Milvus-based vector search pipelines where code naturally spans many modules.