LLMs reward expertise
This article argues that domain expertise is essential for effectively utilizing Large Language Models, countering the idea that AI makes everyone a generalist. It uses mathematician Terence Tao's interaction with ChatGPT as a case study to show how deep knowledge allows users to refine and challenge AI outputs.
Why it matters
It challenges the prevailing narrative that AI replaces the need for specialized skills, suggesting instead that AI amplifies the capabilities of experts.
In the 2010s, if you had technical gaps (say, you couldn’t write CSS), you had to either rely on a skilled colleague or just hope that the answer to your exact problem was out there on the internet. Today, everyone can write sort-of-okay CSS by delegating the task to an LLM. LLMs make everybody into a generalist.
Because of this, lots of people don’t think there’s any skill involved in working with LLMs. If you want the product that LLMs can deliver — PhD-level mathematics, pretty good but sometimes tasteless computer code, or awkward LinkedIn-style writing — you can simply ask for it. Since everyone is talking to the same models, “skilled prompters” are getting the same results as people touching LLMs for the first time.
This is wrong. The most important skill in prompting is expertise in the domain you’re prompting for.
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