AI coding assistants are genuinely useful: they scaffold boilerplate, suggest an approach you had not considered, and turn a vague intent into a running draft in seconds. They are also confidently wrong on a regular basis, producing code that looks right, reads well, and fails in ways that are easy to miss precisely because it looks so reasonable.
The skill is not deciding whether to use them; it is using them without letting their mistakes become yours. That comes down to treating their output as a draft from an eager junior, not a verdict from an expert.
You own every line you commit
The moment you accept a suggestion, it is your code — your name is on the commit and your responsibility if it breaks. That framing keeps the standard where it belongs. Read what the assistant produced as carefully as you would read a colleague's pull request, because that is exactly what it is: a proposal that needs review before it earns your trust.
The failure mode to avoid is accepting code you do not fully understand because it appears to work. Code you cannot explain is code you cannot debug, and it will eventually need debugging. If a suggestion uses an approach you do not follow, understand it or replace it before it goes in.
The specific things to check
Assistants are trained on the average of public code, which means they cheerfully reproduce common insecurities: unvalidated input, string-built queries, secrets inlined for convenience, outdated patterns. Review generated code for the security basics with extra suspicion, because the model has no awareness of your threat model. They also invent things — a library function or an API method that sounds real and does not exist — so verify unfamiliar calls against real documentation.
Edge cases are another blind spot. Generated code tends to handle the happy path and skip the empty list, the null, the concurrent write. Your tests, not the model, are what prove it actually works, so keep writing them.
Where they genuinely shine
Used well, an assistant is a fast collaborator for the parts of the job that are more typing than thinking: repetitive transformations, first-draft tests, explaining an unfamiliar snippet, or exploring an approach before you commit to it. Letting it handle the mechanical work frees your attention for the design decisions that actually need a human.
The developers who benefit most are not the ones who trust these tools the least or the most, but the ones with the judgement to know which is which — leaning on the assistant for speed while keeping their own hands firmly on the standard for what ships.