Why Developer “Taste” Beats Speed When Using AI Coding Tools

What the article covers

The author observes that many engineers use AI merely to accelerate delivery, but the truly skilled ones use it to improve product quality. The core argument is that AI’s poor performance is not a flaw of the models but a symptom of missing developer judgment before hitting the Enter key.

Concrete examples of AI‑generated artifacts

One example describes a fully AI‑generated technical document created by extracting data, feeding it to a prompt, and letting the model produce the text without any investigation, validation, or iteration. Although the document looks structurally complete and fluent, a close read reveals shallow arguments, vague recommendations, and overly simple treatment of difficult topics.

Another case recounts a code change where the model copied an authentication pattern from elsewhere in the repository and inserted it into a service where it did not belong. The change compiled, but because the reviewer understood the service’s architecture, they caught the mistake that the model could not.

Why “taste” matters

Developer “taste” is defined as the ability to decide what is correct before writing any line of code. It is distinct from skill (the ability to implement a solution) and from speed (how quickly code is produced). Engineers with taste ask themselves whether the generated code truly reflects the solution they would write, rather than accepting it blindly.

The author notes that code that merely runs is not enough; it must remain maintainable for months, avoid hidden abstractions, and handle edge cases. Poorly judged AI output can introduce subtle defects that surface later, such as tests that pass in a controlled environment but cause data loss in production.

Five typical AI‑generated code mistakes

Treating AI output as final. Developers paste generated functions directly, run tests that pass, and move on without questioning whether the code matches their intent.

Copying from secondary sources. Models learn from existing code, which may not follow standards. Relying on such patterns without consulting original specifications (RFCs, design docs) leads to fragile solutions.

Skipping problem decomposition. Tackling a multi‑part task with a single prompt causes hidden errors; breaking the problem into smaller, reasoned pieces yields better control.

Only handling the happy path. AI‑generated code often lacks error handling, boundary checks, and tests for abnormal conditions, because engineers rarely ask the model to consider them.

Prioritizing running code over correctness. Getting code to compile is only half the battle; the other half is ensuring it stays correct, concise, reviewable, and understandable over time.

Practical habits to cultivate taste

Think from the outside in. Write integration‑style tests or human‑readable specifications before any code, establishing a clear definition of “done.”

Keep commits small and focused. One commit, one responsibility forces you to articulate the purpose of each change.

Review your own code before handing it off. Pretend you are a reviewer and ask probing questions to uncover hidden assumptions.

Consult original sources. Refer to documentation, standards, and design records rather than relying on the model’s internal knowledge.

Define a framework and let AI fill details. Outline data structures, module boundaries, and signatures first, then ask the model to implement the internals.

Read and write lots of code. Examining “bad” code from others sharpens your ability to spot subtle flaws.

Impact on career trajectories

Engineers lacking taste become interchangeable executors: they can produce large volumes of code but are replaceable once AI can replicate the mechanical parts of their work. In contrast, engineers with strong taste become orchestrators: they define problems, design solutions, and guide AI output, making their judgment increasingly valuable as AI tools improve.

The author concludes that over the next five years, those who invest in cultivating taste will thrive, while those who rely solely on speed and typing will face diminishing relevance.