Vibe Coding Still Requires Engineering Judgment

Source and AI note: This article is based on Gemini’s Devpractice on YouTube. It was generated and edited with the gpt-5.6-sol model.

Vibe coding can make software more approachable and accelerate work in unfamiliar areas. It does not remove the need to understand and define the problem, inspect proposed code, judge available suggestions, and retain one’s own criteria for good code and design.

This is not a dispute over whether AI-assisted work counts as development. The useful distinction is between using AI to extend thought and using it to avoid thought.

Generated choices still need a chooser

An AI tool may offer several implementations. When several suggestions are available, selecting one requires enough knowledge to read them and judge which fits the current situation.

The same applies before code exists. A developer still needs to understand and define the problem before evaluating an answer.

That is why fundamentals remain useful even when less-experienced people can produce more. Technical knowledge is not valuable because typing code must remain difficult. It is valuable because someone has to recognize a broken assumption, reject a poor design, and verify that the result solves the intended problem.

“It works” is not a complete quality standard

A program that runs is not the whole quality standard. Developers still need their own criteria for what makes code and design good; those criteria do not emerge automatically from a natural-language request.

The dangerous form of vibe coding is not rapid experimentation. It is accepting output without thinking or judging whether it meets one’s quality criteria. Dependence grows as the developer gives up that judgment.

AI can also be used to learn and widen one’s thinking rather than to stop studying.

Keep learning while the interface changes

The exact division of labor between people and AI is uncertain. For now, however, vibe coding alone is not enough to carry a paid, business-level engineering role. Developers still need to study enough to frame problems, inspect results, and notice when a tool is wrong.

Natural language can help someone begin work in an unfamiliar area. When several suggestions are available, the developer still has to read and judge them, keep personal criteria for good code and design, and use AI to broaden learning rather than end it.

The practical position is to stay open to the new interface without giving up the ability to think and judge.