Vibe coding can help optimize database query patterns, but “automatically” has limits. The model is good at spotting common anti-patterns in the code you show it—N+1 queries, missing indexes, unbounded scans—and proposing more efficient alternatives. For example, you can paste a repository class and say, “These methods are slow; they cause many small queries. Rewrite them to batch requests and use appropriate indexes.” It can then produce a version that uses joins, prefetching, or bulk operations, following the idioms of your ORM or query builder.
For vector workloads, vibe coding can do something similar if you provide enough context. Suppose you have a service that queries a vector database such as Milvus or Zilliz Cloud. You can ask the model to review your search code and suggest improvements: better batching of embeddings, reuse of clients, tuning of search parameters like nprobe or efSearch, and separating index-building from query-time execution. It can also propose strategies like precomputing some metadata, using partitions, or adjusting top-k values based on the use case. The quality of these suggestions improves a lot if you share rough metrics: latency, QPS, and size of your collections.
However, the model cannot see your actual query plans, runtime statistics, or cluster health, so it cannot replace profiling and measurement. The most effective workflow is still: measure a real bottleneck, share a focused code snippet and a summary of the problem, ask for an alternative implementation, then benchmark the new version. You might also ask the model to generate profiling scripts or observability instrumentation (e.g., logging query latency per endpoint) to help you collect better data. In short, vibe coding is a smart advisor and code generator for query optimization, but the final word should always come from real-world metrics.