Sean Goedecke pushes back on the idea that LLMs make everyone a generalist and that “prompting skill” is a myth. The real differentiator, he argues, is domain expertise. His proof point is Terence Tao’s conversation with ChatGPT about the Jacobian Conjecture counterexample — Tao’s prompts are short, precise, and push back surgically, not because he’s a gifted prompter, but because he understands the mathematics deeply enough to know exactly what to ask for and where to steer. Goedecke connects this to his own experience programming with AI: if you have a good theory of your codebase, you can push the LLM far harder than someone who doesn’t, asking questions like “but don’t we already do X?” or “can we express this problem in these familiar terms?” The practical implication is counterintuitive: as models get stronger, human expertise becomes more valuable, not less. The bottleneck shifts from what the model can produce to what the human can articulate — and only a domain expert can communicate the shape of a good solution. If you have no domain knowledge, you can at least get something from an LLM, and that’s not bad. But if you have expertise, you can wring far more value out of the same model by steering it hard in the direction you want.
LLMs Reward Expertise — Sean Goedecke
Domain knowledge is the real prompting skill — the human, not the model, is the bottleneck as AI gets stronger.