You don’t need to be an expert in every coding topic, but you do need a solid understanding of software fundamentals. Vibe coding works best when you can read and evaluate the code that the AI produces. This means you should understand control structures, data models, API design, and the core libraries used in your tech stack. If the model generates a flawed implementation, you must be able to spot the issue. Think of vibe coding as an accelerant—not a replacement—for your existing engineering judgment.
Another important skill is the ability to write clear, structured, and constraint-focused prompts. Developers who can express requirements precisely get far better results. A vague prompt like “make a search API” will produce inconsistent output, while a precise prompt such as “create a FastAPI endpoint that queries Milvus using cosine similarity and returns the top 10 vectors” gives the model enough structure to create useful code. Skilled vibe coders also know how to iterate: they review output, correct misunderstandings, guide revisions, and expand components step by step.
Familiarity with your domain is also necessary. If you’re working with vector databases, you should understand embedding generation, index types, search parameters, and the performance characteristics of the system you use. For example, if you want vibe coding to build batch ingestion pipelines for Milvus, you should know how partitioning, index creation, and consistency levels work. The more domain knowledge you bring, the more accurately you can instruct the model and evaluate its output. In short, vibe coding rewards developers who understand their tools, express their intentions clearly, and remain hands-on in validating the final result.