The code you cannot explain

A developer asks for a feature and receives code that appears to work. The demonstration is satisfying. But software lives beyond the demonstration: unusual inputs arrive, permissions change, dependencies update, and someone eventually needs to understand why a decision was made.

A demonstration can work while a future maintenance problem is being created. Reading generated code is part of accepting responsibility for the behaviour, not an optional delay after success.

AI assistance can reduce repetitive work and offer useful starting points. It can also produce insecure, outdated, or subtly incorrect code. Passing one visible example does not establish correctness, and a detailed explanation generated alongside the code is not independent proof.

Treat unfamiliar output as a proposal. Read the control flow, inspect data handling, check dependency documentation, and test relevant edge cases. Never paste private credentials or sensitive production information into a tool without an approved data policy.

A useful check follows the risk: unusual inputs, permissions, data handling, and dependency assumptions. The goal is not to distrust assistance, but to know what evidence makes it worth accepting.

Beginners benefit from rewriting a small part themselves and explaining the design to another person. Experienced developers still need to ask whether the generated solution fits the project's conventions and maintenance needs. Speed is valuable only when the resulting obligation is understood.

The strongest AI-assisted developer is not the person who accepts the largest amount of code. It is the person who knows which parts are useful, which are risky, and what evidence will distinguish the two. Ownership begins where the demonstration ends.