> I get the impression that's the same reason their fine-tuning services never took off either
> 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.
Very typical e-commerce use cases processing scraped content: product categorization, review sentiment, etc. where the scope is very limited. We would process tens of thousands of these so faster inference with a cheaper model with FT was advantageous.
Disclaimer: this was in the 3.5 Turbo "era" so models like `nano` now might be cheap enough, good enough, fast enough to do this even without FT.
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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.