We are finding that using context hints injected dynamically into ChatGPT prompts by way of separate classifiers is potentially the ultimate solution for many b

We are finding that using context hints injected dynamically into ChatGPT prompts by way of separate classifiers is potentially the ultimate solution for many business needs. Having the very first thing be an explicit natural language feature parsing phase can make the downstream generative stuff way more predictable. We could actually go back and look at the traces around a user's original prompt to determine why it might have generated nonsense downstream. The challenging part of this is inverting everything and figuring out what all of your classes are and how to arrange training data to fit them. Set theory is another new dragon here. Smaller models like Ada and Babbage are ideal for this, but we are looking at 90's tech too (SVMs). If we need 10k separate models because we have so many classes, then I'd prefer to be able to train them in a few seconds/minutes on a CPU. Fine tuning gigantic models like Davinci is a dead-end at this point IMO. A fleet of microscopic models working together to feed 1 big powerful (untunable) model feels a lot more manageable.