The line about ignoring him for two weeks because fine tuning felt like real AI work really hit me. I did the exact same thing last year with my recipe collection, spent a whole weekend messing with learning rates and batch sizes and my model kept spitting out garbage that sounded confident. Finally broke down and just embedded everything, and suddenly it was pulling the right recipe and actually answering questions about substitutions. The part that stung was realizing I wasn't bad at training, I was just training for the wrong problem. Facts live in the documents, the model doesn't need to memorize them. That $60 vs $4 gap is real too, I think people just like the feeling of "building" something even when throwing chunks at context does the job better.