One of the posts was about this company Openpipe that makes it easy to finetune models (using LoRA under the hood), so you can reduce inference cost vs. using the GPT-4 API.
Something I read said prompting will be replaced by adapters.
That made me curious about how finetuned models are prompted when put into production. For example, let's assume I'm currently using GPT-4 with a long prompt that includes a dozen examples of correct inputs+outputs (n-shot learning), and then finally the current input.
Should the fixed part of this long prompt be included in the dataset used for fine-tuning? Or just the variable part?
Once the finetuned model is in production, do I still need a system prompt or similar to describe my task on each call, or do I just sent the input data?
I came across this article when I was searching for an answer to these questions. It doesn't cover specifics of prompting the finetuned model, but it's such a nice write up that provides enough detail to replicate what the author did, and also gives us some insight into why he made those choices.