AI doesn't introduce fewer bugs than a human — it introduces different ones. Accepting a full diff without structured review is the fastest way to accumulate invisible technical debt.
Code generated by Cursor doesn't fail the same way code written by a rushed human does. The typical error patterns are: logic that "looks" correct because it follows a common pattern but doesn't fit the project's specific business rules, generic error handling that swallows important exceptions, and duplication of logic that already existed in another file because the model didn't find it in its context. None of these necessarily produce a visible failure in development — they show up in production, with real data, in the edge case nobody tested.
It sounds obvious, but Cursor's friction is designed so that accepting is the path of least resistance (a single Tab or click). Force a structural pause: review the full diff in the changes view before applying, not in the chat.
If the count of affected files is higher than expected, it's a sign the model over-generalized beyond the requested scope.
On top of the normal review (style, tests, readability), add checks that target LLMs' typical errors:
- Real error handling, not generic. Look for empty try/catch blocks or ones that only do console.log(error) without propagating or logging into the project's observability system. - Boundary validation and null-checks. Models sometimes assume the "happy path" (non-empty arrays, always-present fields) and skip validations the rest of the code does respect. - Duplication of existing logic. If the model wrote a new email-validation function, check whether one already exists in utils/ — a common pattern when the context didn't include that file. - Unrequested new dependencies. Check whether a library got added to package.json or composer.json that you didn't discuss; models sometimes "solve" a problem by installing something instead of using what's already available.
Once the change is applied, ask for an explicit review in a separate prompt, not in the same conversation where the code was generated — this prevents the model from simply rubber-stamping its own work out of conversational momentum.
This doesn't replace human review, but it catches a category of mechanical errors before they reach the human reviewer, who can then focus on design decisions instead of syntactic details.
If the generated code doesn't come with tests, write them yourself or explicitly request them before merging — never assume "it looks fine" is enough. For critical business logic (payments, authentication, permissions), require that the original prompt include generating tests covering specific edge cases, not just the happy path.
On teams where several developers actively use Composer, add a simple convention in the PR description: which prompt (summarized) generated the main change. This isn't bureaucracy — when a bug shows up three weeks later, knowing the code came from a specific prompt helps diagnose whether the problem was an ambiguous instruction, insufficient context, or simply a case nobody considered.
The golden rule no team should relax: the developer who approves the merge is responsible for the code, no matter who (or what) wrote it. Cursor is a tool that speeds up writing, not a substitute for the responsibility of understanding what's being deployed to production. Treating AI code with more skepticism, not less, than your own code is the discipline that separates teams that gain real speed from teams that accumulate incidents.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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