Hello!
I would like to train OpenAI's Davinci model on a company's knowledge base, which consists of a few hundred articles and transcribed videos. I want to use this model as a chat support helper tool that understands the user's questions and feeds me intelligent and factually correct replies. I provide tech support and would like to make my work easier.
Their documentation on fine-tuning makes it seems like it's more about learning entirely new skills than just adding more information. It recommends using multiple examples and the case study of a customer support chatbot uses a different structure from what I need:
{"prompt":"Summary: <summary of the interaction so far>\n\nSpecific information:<for example order details in natural language>\n\n###\n\nCustomer: <message1>\nAgent: <response1>\nCustomer: <message2>\nAgent:", "completion":" <response2>\n"} {"prompt":"Summary: <summary of the interaction so far>\n\nSpecific information:<for example order details in natural language>\n\n###\n\nCustomer: <message1>\nAgent: <response1>\nCustomer: <message2>\nAgent: <response2>\nCustomer: <message3>\nAgent:", "completion":" <response3>\n"} Are their finetunable models what I'm looking for or should I be using something else? What prompt structure should I use if I want it to feed answers to questions like ChatGPT would?
Thanks a lot!
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