Something that has become clear watching different people use the same tools on the same task and get very different results. The output quality tracks the user’s existing domain knowledge almost exactly. Someone who already understands a topic can prompt precisely, catch the model’s mistakes, and push it toward something good. Someone who does not know the topic cannot tell a solid answer from a confident wrong one, so they ship whatever comes out. Same tool, opposite outcomes. This runs against the common hope that these models level the field for people without expertise. In practice they seem to widen the gap. The expert gets a real multiplier. The novice gets fluent output they cannot evaluate, which can be worse than having no output at all because it looks trustworthy. If that holds, the skill that matters most going forward is not prompting. It is knowing enough to judge the answer. Do people here see the multiplier-for-experts pattern, or are there domains where the tools genuinely help the uninformed get to a right answer. submitted by /u/EnvironmentalDay8864
Originally posted by u/EnvironmentalDay8864 on r/ArtificialInteligence