You verify correctness in vibe coding by treating all generated code as a first draft that must pass the same tests, reviews, and validation processes you would apply to any human-written code. The model produces logic based on patterns and instructions, but it cannot guarantee that its output matches your business rules, performance expectations, or edge-case behavior. The most reliable approach is to validate the output through unit tests, integration tests, linters, and type checkers. Many developers even ask the model to generate test cases alongside the code, which helps create immediate feedback loops for correctness.
Another important method is incremental generation. Instead of requesting an entire feature in a single prompt, developers break the problem into small, testable components. For example, if building vector-search functionality, you might first ask the model to generate a Milvus schema definition, then a batch ingestion function, and then a search query method. After each step, you verify that the output works as expected, ensuring that each layer is correct before moving on. This pattern keeps the system grounded in real behavior rather than abstract assumptions and makes debugging far easier.
Finally, correctness benefits from repeated context reinforcement. When the model generates logic that interacts with existing parts of the codebase, developers should paste relevant modules or provide clear summaries. This helps the model stay consistent with existing types, naming conventions, and interfaces. For example, if your Milvus search method must accept embeddings of a specific dimension, including the schema details in your prompt prevents accidental mismatches. By combining incremental development, automated testing, and explicit context-sharing, you can verify correctness effectively and treat vibe coding as a productivity tool rather than a risk.