1. I could see that actually being somewhat analogous to how data work evolved? It's not a perfect fit, but in the early days of BI, you were somewhat restricted on what you could do; it wasn't really open-ended calculations, and especially not on raw data. But, as the tech evolved and improved, we could do that - at first, slowly, on small datasets, and on increasingly bigger ones. I could see the same thing here, where tools like Gong do it with guardrails; then, we can do it manually on small stuff; then, bigger stuff.
2. I agree that the individual examples are really useful, though it seems like we could actually do that with LLMs too? This is maybe a wilder idea, but if "summarize" is like an "average," could there be a "median?" Take these 100 tickets, and find me the one that best captures the overall themes in the full dataset. Or, like, MIN and MAX? Of these 100 review, find the 10 that are most unusual? I don't know exactly how this might work (or if an LLM could do anything remotely like it), but I'm sure if you gave researchers 1,000 video interviews and said "What can we do to help you make sense of this?," they'd come up with bunch of interesting "functions."
*I realize I mentioned gong a lot... note I don't have any association with gong or make any money as an affiliate of gong. 🙃
So that example makes me think of two things:
1. I could see that actually being somewhat analogous to how data work evolved? It's not a perfect fit, but in the early days of BI, you were somewhat restricted on what you could do; it wasn't really open-ended calculations, and especially not on raw data. But, as the tech evolved and improved, we could do that - at first, slowly, on small datasets, and on increasingly bigger ones. I could see the same thing here, where tools like Gong do it with guardrails; then, we can do it manually on small stuff; then, bigger stuff.
2. I agree that the individual examples are really useful, though it seems like we could actually do that with LLMs too? This is maybe a wilder idea, but if "summarize" is like an "average," could there be a "median?" Take these 100 tickets, and find me the one that best captures the overall themes in the full dataset. Or, like, MIN and MAX? Of these 100 review, find the 10 that are most unusual? I don't know exactly how this might work (or if an LLM could do anything remotely like it), but I'm sure if you gave researchers 1,000 video interviews and said "What can we do to help you make sense of this?," they'd come up with bunch of interesting "functions."