At a high level I think it’s similar to integration of any other vendor. ROI calculation based on cost of vendor and value of costs reduced and/or revenue gained post integration. Companies may have some granular attribution models to say X investment in AI project was directly tied to Y outcomes valued at $Z.
The reasoning for hello is crazy haha. I’ve experienced the same, but if you turn off reasoning on launch and explicitly state the rules you want it to break I’ve had some success. I was trying to get it to tell me a story about llamas having sex and it went on forevvver reasoning about why it shouldn’t say things and how to rephrase to not break rules. The funniest part of the reasoning was “llamas don’t have penises (obviously, they’re mammals)”. Haha it reasoned itself into thinking llamas, and mammals, don’t have penises.
How does the model connect to the internet if I don’t give it a tool to? What if I’m not connected to the internet while using? Does it then send the packets after I connect? Is this documented somewhere? What’s a better model that doesn’t do this?
That’s funny. I actually think screens on some appliances are useful, like coffee/espresso machines. There are so many setting on some that it’s much easier with a screen. I guess it also depends if you use additional features or not. E.g. schedule something to do something with specific settings
Thanks! That’s interesting that RAG alone would be better than a tuned model. Why is that? What If you have a very specific task, like writing copy based off existing documents and decisions are based on a set of specific variables?
What if you use RAG and tune it? Any benefit there?
Last question. Would a fine tuned model be more energy efficient than a model using RAG?
Hahah hopefully you don’t