AI-assisted development works best when the feedback loop is short. One change. One test. One review. One clear result. Large generated diffs make it harder to identify which assumption went wrong.

A useful way to use AI for debugging: Give it the error, the relevant code, the expected behavior, and what you've already tested. Then verify its hypotheses one at a time. Debugging is investigation, not a contest to generate the most suggestions.

AI can help you move faster. But speed creates another responsibility: knowing when to stop adding features. A prototype can grow endlessly. A product needs priorities, constraints, and a clear reason for every important decision.

If a customer needs to contact support to understand the status of an order, that's a product signal. Maybe the status labels are unclear. Maybe updates are delayed. Maybe the next step isn't obvious. Support questions can reveal UX problems worth fixing.

A background job should have a clear owner for failure. Who notices it failed? Can it be retried safely? Is the error visible? Does someone need to intervene? A queue moves work around. It doesn't automatically make work reliable.

One of the most important API design decisions is deciding what not to expose. Internal database structure isn't automatically a good public response format. A clear API contract gives you room to change the implementation without breaking every consumer.

AI can generate a database schema from a product description. But product descriptions often leave out retention rules, access boundaries, audit requirements, and relationships that only become obvious later. Use AI to accelerate design exploration, then validate the model against real workflows.

When AI suggests a refactor, ask one question: What measurable problem does this solve? If the answer is unclear, you may be trading working code for a more complicated version of the same thing.