[AlphaAndOmega0]

I found it genuinely impressive how useless their "GPTs" were.

Of course, part of it was due to the fact that the out-of-the-box models became so competent that there was no need for a customized model, especially when customization boiled down to barely more than some kind of custom system prompt and hidden instructions. I get the impression that's the same reason their fine-tuning services never took off either, since it was easier to just load necessary information into the context window of a standard instance.

Edit: In all fairness, this was before most tool use, connectors or MCP. I am at least open to the idea that these might allow for a reasonable value add, but I'm still skeptical.

[CharlieDigital]

    > I get the impression that's the same reason their fine-tuning services never took off either

Also, very few workloads that you'd want to use AI for are prime cases for fine-tuning. We had some cases where we used fine tuning because the work was repetitive enough that FT provided benefits in terms of speed and accuracy, but it was a very limited set of workloads.

[apwell23]

> fine tuning because the work was repetitive enough that FT provided benefits in terms of speed and accuracy,

can you share anymore info on this. i am curious about what the usecase was and how it improved speed (of inference?) and accuracy.