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AI assistants are now a normal part of SQL work. You describe what you need, get a query back in seconds and move on. The problem is not that these queries fail. Most of the time, they run perfectly. The problem is that a query can run perfectly and still be wrong. This post covers why that happens, how to sort database tasks by risk and how to build guardrails into a normal workflow.
Indexes tend to outlive the problems that created them. It usually starts with a slow query. An engineer adds an index, the immediate issue goes away, and everyone moves on. At the time, the decision makes sense. The problem is that the index often stays in place long after the application changes, a report is replaced, or a feature quietly falls out of use. Over time, a busy table can collect a long list of nonclustered indexes. Some are still doing useful work.
Developer productivity is not just about writing code faster, it is about removing the friction around the work. In SQL development, that friction often comes from small, repetitive tasks. A developer opens SSMS, starts a stored procedure, and ends up rebuilding a TRY/CATCH block, transaction handling, logging, or a few common JOINs they have already written many times before.
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