The main limitation of vibe coding is that the quality of the output depends heavily on prompt clarity and developer oversight. While the model can generate structured code that looks correct, it may miss edge cases, produce inconsistent naming, or introduce subtle bugs when integrating multiple components. This risk increases in large applications where many modules interact. Developers must validate the output line by line and ensure the generated code aligns with actual business logic. This is especially important when building vector search systems where embedding consistency, index configuration, and query correctness directly affect application behavior.
Another limitation is long-term consistency. The model does not maintain persistent memory of your codebase unless you actively show it the relevant files. This means it can generate outdated imports, mismatched parameter names, or inconsistent logic if earlier versions of files are not provided. As a result, vibe coding works best when the developer periodically prompts the model with up-to-date summaries, directory structures, or key class definitions. In multi-module applications involving Milvus—where ingestion, indexing, and search components must align—this form of manual context management becomes even more important.
Finally, vibe coding struggles with tasks requiring deep domain knowledge or nuanced trade-offs. It can scaffold a Milvus ingestion pipeline, but it cannot reliably choose the ideal index type, memory allocation, or search parameters for a specific workload. It can generate authentication code, but it might forget important security measures unless explicitly asked. These limitations mean vibe coding excels at accelerating boilerplate and producing structured components but cannot replace engineering judgment. Developers must still design architectures, enforce security policies, and validate performance-critical logic.