I'm interested in this as well and maybe to broaden the scope of the question a little:
If I have a sizeable amount of labeled data and need decisions calibrated to that data should I
1. Ignore the hype and train a traditional classifier
2. Finetune an LLM based decision model
3. Shoehorn (probably a small subset of) the data into the context of the LLM classifier somehow
If the answer is 3. where does the data belong? In the input content? Request wide state? In the question instructions? In the criteria? How much of my data can and should I use?
If I have a sizeable amount of labeled data and need decisions calibrated to that data should I
1. Ignore the hype and train a traditional classifier
2. Finetune an LLM based decision model
3. Shoehorn (probably a small subset of) the data into the context of the LLM classifier somehow
If the answer is 3. where does the data belong? In the input content? Request wide state? In the question instructions? In the criteria? How much of my data can and should I use?