Yeah, I've always been impressed with how well GPT3 can give cogent responses, but I've never seen anyone show how to get it to give truthful, informative responses while behaving as a chatbot. Could you feed structured data into the prompt text? like average response rates in the customers area, whether there's capacity to support, the state of engineering teams?
Having never seen anyone try it, my gut says it will work reasonably well outside of already known failure modes. (The tendency to loop, make up stories, or joke/cuss people out)
Yes, there is a line of research combining passage retrieval with question answering. The query is used to rank passages in a database. The top-k passages are concatenated to the question and used as input by GPT to generate an answer. This means you can keep the model fixed and update the text corpus. Also, you can separate linguistic knowledge from domain knowledge.
I think a new type of apps are going to popularise this: a language model + a personal database + web search. It can be used to recall/summarise/search/ information, a general tool for research and cognitive tasks, a GPT-3 Evernote cross breed.
Having never seen anyone try it, my gut says it will work reasonably well outside of already known failure modes. (The tendency to loop, make up stories, or joke/cuss people out)