Reviewing pull requests (PRs) containing vibe-coded output requires a shift in focus from traditional code review. The primary goal is to verify architectural coherence, dependency accuracy, and security, rather than just checking syntax against a style guide. The reviewer should start by understanding the developer's intent, ideally from the prompt or task description that generated the code . The review must then scrutinize how well the new code integrates with the existing system, ensuring it uses established project libraries and patterns correctly and doesn't introduce redundant or conflicting dependencies.
A critical area of focus is identifying "hallucinated" or misplaced logic. AI-generated code can be syntactically perfect but semantically flawed . The reviewer must actively look for these issues, such as the use of non-existent environment variables, internal functions, or outdated API calls. A common example in the context of vector databases is code that uses a deprecated connection method like connections.connect() for Milvus instead of the modern MilvusClient . The most powerful tool here is testing. The reviewer should insist that the PR includes not just unit tests but also small, runnable integration tests. A test that demonstrates a full workflow—from ingesting a sample document to querying and receiving a correct result—provides concrete proof that the generated code works as expected within the real environment .
Finally, the review process must enforce long-term maintainability. This involves checking for hard-coded values, assessing the clarity of the code for future developers who may not have the original context, and ensuring the code doesn't introduce technical debt through suboptimal patterns . For example, a generated function that performs a vector search might be functionally correct but lack logging, making it difficult to debug in production. The reviewer's role is to ask these "what if" questions: What if the embedding service is down? What if the result set is empty? By focusing on integration, accuracy, and robustness, the review transforms a potentially rapid but chaotic development output into a reliable part of the codebase.