Trainig a GPT-3 is supposed to cost more than 10 millions of dollars, the cost of their API is not so cheap and they still struggling with bias.

Is it possible to build an architecture able to perform at the same level, in a "budget" ?
Tools like Microsoft LUIS are dead? (Quick answer: No!)
How a good dataset worth 100 times more than an array of GPUs computing massive data.?

Starting from resources like hugging face we will access pre-trained models and libraries that will speed up our job.

Finally we will talk about the final architecture that merges GPT-x with (several) Microsoft LUIS (Basically a QnA maker) in production able to perform as well as very expensive solutions (sometimes far away better) .