AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。
The following article originally appeared on Sean Goedecke’s blog and is being republished here with the author’s permission. 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. A good illustration of this is Terence Tao’s conversation with ChatGPT about the recently discovered counterexample to the Jacobian conjecture. This is not the same ChatGPT I talk to! I couldn’t get to where Tao gets, even with unlimited tokens to burn. There’s a lot to learn about good prompting from Tao’s conversation. Here are a few observations: Tao’s messages are very short and to the point. He doesn’t respond point by point to the model, just to the gist. The model outputs are much more concise than when I try to talk to GPT-5.6 Sol about mathematics. By signalling expertise, Tao shunts the model into “talking-to-mathematicians” mode, not “explaining-to-amateurs” mode. Tao pushes back when the model’s responses look wrong, but he doesn’t directly contradict; instead, he says things like “This looks more complex than I was hoping for.” Tao makes several leaps and suggestions himself. He almost never takes the model’s advice about where to go next. However, you can’t prompt like Tao on mathematical questions just by following these tips. The key to his technique is actually understanding the mathematics: pulling the relevant idea out of ChatGPT’s multiparagraph response, suggesting alternate approaches or formulations, and identifying what “looks weird.” Terence Tao is a better mathematician than I am a programmer. But the idea here—that domain knowledge makes you better at using LLMs—is something I’ve also experienced in my own work. If you have a good theory of your codebase, you can push the LLM much harder than if you have no familiarity. Because you have your own sense of what a good solution might look like, you can say, “No, I think it could be simpler here” or “But don’t we already do X?” or “Can we express this problem in these familiar terms?” This touches on an idea I’ve written about before: that system design problems are dominated by concrete specifics, not generic principles. Of course both are useful, but I’d rather have familiarity with the codebase than a deep general understanding of software systems. In his conversation, Terence Tao asks a lot of specific questions like “Does X work here?” or “Given Y and Z, why A?” I can’t ask those questions about the Jacobian conjecture, but I can ask them about the systems I own at GitHub. If you have no domain knowledge, you can cling onto the LLM to at least get something. That’s not bad! But if you have domain knowledge, you can wring far more value out of the same LLM by steering it hard in the direction you want. Most of us will have to do a mix of both these approaches, since we have domain knowledge in some areas but not others. The usefulness of domain knowledge suggests that human expertise will continue to be useful even as models get stronger. For many tasks, the human is the bottleneck, not the model, because the difficult part is in communicating to the model exactly what kind of solution the human wants. The information is “in the model” already, but it takes a very smart human to pull it out. This post got many comments on Hacker News. Some commenters share their anecdotes about how expertise has helped and lack of expertise has hurt. Other commenters say it’s plausible, but they have a sensible suspicion of a view that’s reassuring them about how they’re still valuable. I agree with that, though I suspect by the time we get around to studying this, the landscape will have changed under our feet again. Some commenters point out that OpenAI’s math prompts were inexpert, and so expertise isn’t required. Here I’d respond that OpenAI does have a team of expert mathematicians that checked and filtered the model’s suggested discoveries, and that you cannot currently skip that step.