Vibe coding explained: what it is and why the fundamentals still matter

Vibe coding means creating software largely by describing the desired outcome to an AI tool and accepting the generated code with little review. This makes software development faster and more accessible, but it also creates a significant risk: code that appears to work may contain flaws the user does not understand. Safe AI-assisted development still depends on someone being able to assess what the tool has produced.

What is vibe coding?

Vibe coding uses natural language instructions to tell an AI coding tool what to build. Instead of writing and examining the code in detail, the user focuses primarily on the required outcome and prompts the tool to make changes until the software behaves as expected.

The term was coined by AI researcher Andrej Karpathy in February 2025 and has spread alongside tools such as GitHub Copilot and general-purpose AI assistants such as ChatGPT. These technologies make it possible to generate substantial amounts of code without manually writing every line.

The important distinction is how that generated code is treated. In disciplined AI-assisted development, a developer may also ask AI to create code, but they remain actively involved. They inspect the output and challenge questionable decisions before accepting it.

Vibe coding reduces that scrutiny. If the application appears to work, the user may have little reason to investigate further. This is why programming fundamentals matter even when AI handles much of the initial code generation: understanding the output is what makes meaningful review possible.

Is vibe coding safe?

Vibe coding is only as safe as the review process behind it. Code that runs successfully is not necessarily well designed or secure.

Our recent demonstrated this using a simple Java program generated by AI. The requested program worked and produced the expected output. However, when the trainer examined the code more closely, he found decisions that deserved questioning.

For example, the AI had made a class final without being asked. When challenged about the choice, it gave an explanation that incorrectly linked this decision with immutability and thread safety. Further questioning exposed other assumptions that had not appeared in the original requirement.

In another part of the demonstration, questioning the AI prompted it to change code that it had previously generated, even though it had not been asked to rewrite it. The exchange showed how plausible output and confident explanations may shift when challenged.

A developer who understands the underlying concepts has grounds for asking those questions. Someone accepting the result because it works may never discover that there is anything to question.

Common vibe coding vulnerabilities and security risks

Security becomes particularly important when AI-generated code moves beyond experimentation into software that processes real information.

AI tools respond to the requirements they receive. If security requirements are absent or incomplete, generated code may lack appropriate validation or use insecure defaults. Authentication is another area where a superficially functional solution may fall short of actual production requirements.

Dependencies deserve similar scrutiny. An AI tool may suggest packages or libraries as part of a solution, but developers still need to establish what they are introducing into the codebase. This includes checking the source and licence as well as considering security.

The broader issue is visibility. If a developer does not understand the generated code, reviewing its security becomes considerably harder. Prompting an AI tool to fix a problem is different from understanding why the problem arose and whether the proposed correction is appropriate.

Established secure coding practices therefore remain relevant to AI-generated software. AI changes the way some code is produced; it does not remove the need to examine what enters a production system.

Vibe coding vs disciplined AI-assisted development

The difference between the two approaches lies less in the tool being used than in what the developer does with its output.

Disciplined AI-assisted development combines the speed of code generation with human technical judgement. Developers do not need to reject AI-generated solutions simply because they did not write them themselves. They do need enough knowledge to decide whether those solutions belong in the software.

This also changes the role AI plays in learning. During the StayAhead webinar, we recommended asking AI to explain unfamiliar code rather than accepting it blindly. If the explanation seems questionable, further prompts may expose inconsistencies. The developer then has a reason to verify the answer against reliable documentation.

That questioning habit creates a clear dividing line between using AI to accelerate development and allowing it to replace understanding.

Vibe coding may be useful for experimentation or rapid prototyping where the consequences of failure are limited. As soon as software needs to be maintained or trusted with real data, greater scrutiny becomes necessary. Coding fundamentals provide the knowledge needed to apply that scrutiny effectively.

StayAhead's Coding with AI course explores how to work with generative coding tools while retaining the technical judgement needed to evaluate their output.

Learn to code with AI safely.