There are boundaries that can be defined, but I'm unclear on what those should be. I'm thinking breaking it down by semantic domains, but not at the exclusion o

There are boundaries that can be defined, but I'm unclear on what those should be. I'm thinking breaking it down by semantic domains, but not at the exclusion of others altogether-- just heavily weigh certain domains in favor of others. Kinda like how StackOverflow is broken down by domain-- each subdomain could be a LORA or something. General purpose models containing significant overlap between Project Gutenberg and Github are unnecessary and don't scale. Moby Dick has little to do with C++ unless you're creating art for novelty's sake. This is entirely speculative, but I'm convinced ChatGPT is faking the appearance of a single oracle while delegating requests to specialized models under the hood. It scales better and makes sense than trying to serve a 1T model to address everybody's banal questions. Like, at its core, for people who only want to write literature, give them a model with underweighed programming-related corpora. Writers don't need it, will never use it, and that space could be filled with training content relevant to literature. Anything else results in expensive, unscalable solutions or jack-of-all-trades, master-of-none outcomes. In recent usage, GPT3.5 helped me hack my way through writing Pester tests for Powershell scripts for the first time, and I mean hack-- there were a lot of assumptions it made and things it got wrong. GPT4 did a much better job, but I couldn't help but think 3.5 probably has a ton of other training data in it that detracts from the specialization I needed from it in that context. For coding help, you don't want to ask some random librarian who occasionally recommends resources that don't exist; you ask someone who specializes in coding and trust they have familiarity with that domain.