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Jaclyn Blute
pro
• Jun 22
Knowing enough to catch AI errors is its own expertise.
On a current project, part of my responsibilities involves generating keywords for metadata search functionality in an industrial product database. The domain isn't mine. I don't have the technical background to build that taxonomy from scratch — and I knew that going in.
So I used their AI systems to generate the first pass. And then I exercised my judgment to evaluate the output.
Flagging when a term was too generic to be useful. Catching when the model conflated two distinct product categories because the surface language looked similar. Knowing which gaps to research more.
That's not a workaround. That's the workflow.
AI got me to a starting point I couldn't have reached alone. My judgment determined what was actually usable. This emerging technology doesn't necessarily require you to be the domain expert for every task. It requires you to be accountable for the output.
That's the part that doesn't get said enough: You don't have to know everything. You have to know enough to catch it when it's wrong.
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