Vibe coding changes the development workflow by shifting much of the early manual coding into a conversational planning stage. Instead of opening an editor and beginning with blank files, you start by describing the system you want to build. This might include data structures, routing behavior, integration tasks, or deployment requirements. The AI generates the first draft, and you spend more time reviewing, refining, and adjusting than typing everything from scratch. For many developers, this reduces the time spent on boilerplate and repetitive setup work.
Another noticeable change is the increased emphasis on iterative refinement. Developers often generate code in small parts rather than all at once. For example, you might first ask for a class definition, then request integration with Milvus, then ask for a batch-upsert utility, and finally ask for unit tests. This stepwise approach gives you control and ensures the model stays aligned with your design choices. The workflow begins to resemble pair programming, where you delegate certain tasks to the model but retain responsibility for shaping the architecture.
Vibe coding also alters how developers test and verify their systems. Because code generation is fast, developers run validation earlier and more frequently than in traditional workflows. Automated tests, static analyzers, and type checkers become essential in catching subtle issues. In projects using vector databases, vibe coding helps developers generate ingestion scripts, query interfaces, and indexing jobs, but they still test search quality, embedding consistency, and throughput. The overall effect is a workflow that’s more conversational, more iterative, and more focused on verification than manual construction.