G’day everyone,
I want to use my customer feedback on a recommendation to provide new recommendations based on it, near real time.
Imagine I provide a list of 5 recommendations. My customer tag 2 as ‘bad’. I want to send it back to OpenAI so I receive more recommendations - however it will understand that i sent two ‘bad’ examples to this customer.
The way i thought about doing it is through fine-tuning a model, however I am not trying to generalize to my whole customer base. I am trying to be specific to a customer.
To work-around this, I thought about providing the customer identification in the prompt, so my fine-tuned will understand this specificity.
Do you guys think it should work? I would like to hear thoughts about the solution:
- Provide recommendations to my customer.
- Collect good and bad examples.
- Fine tune the model with something like: For my customer (#client_identification), the recommendation {recommendation} is bad. For my customer (#client_identification), the recommendation {recommendation} is good.
- Request other recommendation from the model, different from the previously done suggested in the tuning phase.
Thanks everyone and as long as I evolve this idea, i will certainly provide some updates!