Vibe coding is fun — here is what it takes to ship AI-generated code to production

The rise of AI-assisted development has made it easier than ever to spin up working prototypes in minutes. A prompt goes in, code comes out, and suddenly you have something that “works.” This phenomenon-often called “vibe coding”-prioritizes speed, intuition, and iteration over rigor. It is powerful, but it creates a widening gap between what feels done and what is actually ready for production.

That gap is where senior engineers spend most of their time.

A prototype generated by AI often solves the immediate problem: it runs, it produces expected outputs in basic cases, and it may even look clean at first glance. But production code is not judged by whether it works once. It is judged by whether it continues to work under stress, misuse, scale, and time. Bridging that gap requires a different mindset-one grounded in verification rather than generation.

One of the first areas senior engineers scrutinize is correctness beyond the happy path. AI-generated code frequently lacks robust error handling and edge case coverage. Inputs that are malformed, unexpected, or adversarial can easily break logic that seemed solid during initial testing. Engineers will ask: What happens if this input is null? Too large? Maliciously crafted? They expect explicit handling, not assumptions.

Security is another critical checkpoint. AI tools are notorious for producing code that appears functional but quietly introduces vulnerabilities. Common issues include unsanitized inputs, insecure deserialization, weak authentication flows, and accidental exposure of sensitive data. A senior engineer will review data flow carefully: where inputs originate, how they are validated, and whether outputs could leak information. They are not just asking “does it work?” but “how could this be exploited?”

Performance is also a frequent blind spot. AI-generated code often favors clarity over efficiency, which is fine in prototypes but dangerous at scale. Nested loops, redundant database queries, or unbounded memory usage may not show up in small tests but can cause failures in production. Engineers will evaluate time and space complexity, database access patterns, and concurrency behavior. They are looking for signs that the code will degrade under real-world load.

Testing is where the difference between prototype and production becomes most visible. Vibe-coded solutions often come with little or no automated testing. Senior engineers expect unit tests, integration tests, and sometimes property-based or fuzz testing depending on the domain. They also look at test quality: do the tests meaningfully assert behavior, or do they simply mirror the implementation? Good tests challenge the code; they do not just confirm it.

Readability and maintainability are equally important. AI can produce verbose or inconsistent code that technically works but is difficult to understand or extend. Engineers review naming conventions, modularity, and adherence to team standards. They ask whether a future developer-who did not write the prompt-can quickly grasp and safely modify the code. If not, it is not ready.

Finally, there is the question of ownership and intent. AI-generated code can obscure why certain decisions were made. Senior engineers often push for simplification or refactoring, not because the code is broken, but because its reasoning is unclear. In production systems, clarity is a feature. Code should communicate its purpose, not just execute it.

The core tension is this: AI accelerates creation, but production demands accountability. Vibe coding is an excellent way to explore ideas and bootstrap solutions, but it does not replace the engineering discipline required to make software reliable. The most effective teams are not those who avoid AI, but those who treat its output as a starting point-subject to the same scrutiny, testing, and refinement as any human-written code.

Closing that gap is not about slowing down. It is about knowing when to switch modes: from generating to validating, from trusting to verifying, and from “it works” to “it holds.”
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Rizwan Saleem — https://rizwansaleem.co