LLMs reward expertise

Large language models reward domain expertise rather than eliminating skill gaps, as demonstrated by mathematician Terence Tao's superior ability to extract insights from ChatGPT compared to novices asking the same model. While LLMs democratize access to generalist capabilities, truly effective prompting requires deep knowledge in the specific field—whether mathematics or software engineering—to recognize errors, suggest better approaches, and guide the model toward higher-quality solutions. The most skilled users leverage their expertise to critically evaluate and steer LLM outputs, rather than simply accepting what the model generates.