> The solution: create your own toolchain No thanks. I have an actual job & customer needs to tend to. I am about 80% of the way through integrating with the OAI assistant API. The real secret is to already have a viable business that AI can subsequently improve . Making AI the business is a joke of a model to me. You'd have an easier time pitching javascript frameworks in our shop. Our current application of AI is a…
Did you read the article or are you responding to what you imagine it says? > a whole toolchain of specialized models, … all of these specialized models are combined with tons of just normal code and logic that creates the end result They are not referring to a toolchain as “write a compiler”. They are referring to it as “fine tune models with specific purposes and glue them together with normal code”. It’s a no-brai…
> will be technically out competed by their competitors because fine tuned models are better.
I disagree that fine tuning is the way to go. We spent a large amount of effort on that path and found it to be untenable for our business cases - not from an academic standpoint, but from a practical data management/discipline standpoint. For better or worse, we don't have super clean, structured data about our business. We also aren't big enough to run a full-time data science team.
Picking targeted feature verticals and applying few-shot learning w/ narrowly-scoped, dynamic prompts seems to give us a lot more value per $$$ and unit time. For us, things like the function calling API are fine-tuning, because we can now insist that we get a certain shape of response.
I have a hard time squaring an implied, simultaneous agreement with "supermodels are generally not the right approach" and "fine tuned models are better". These ideas seem (to me) to be generally at odds with one another. Few-shot learning is still the real magic trick in my book.