Hebbia's George Sivulka on why good prompting means understanding the work so deeply you could explain it like Feynman:
"For the majority of human tasks done day to day, you can already do almost all of them with AI. The issue is that the AI is not being prompted correctly. With almost a GPT-4 class model and the right prompting and the right process engineering, you could get to most of the valuable economic output of human beings."
"People just haven't been able to crack the actual process engineering, the prompt engineering, and the change management required to get there. So we're waiting for new models, expecting them to do more, but it doesn't really matter if Claude Fable 5 can build a video game in the browser better than Claude Opus 5, or if the next great OpenAI model can create some weird WebGL simulation."
"These aren't really as economically valuable tasks as the things you could probably already do if you had the right prompt engineering with a GPT-4 class model."
"What does it take to prompt effectively? There's some element of practice, some element of wordsmithing. But it's really about sense-making, human beings so in command of the language, or so knowledgeable about a domain that they feel it in their bones, that they can explain very complicated concepts to a five-year-old."
"Richard Feynman was one of those human beings. He could teach a quantum physics class to the average person and they'd actually learn something. It wasn't that he was a better physicist, it's that he understood the concepts and could sense-make around them so elegantly that he could portray his work simply."
"So it's less about crafting a prompt as writing, and more about understanding the work so deeply that you feel it in your bones. The writing and the eloquence comes out of that."
@gsivulka @hebbia