This is the first in a series of articles I'm writing to introduce devs to practical applications of large NLP language models (for text generations like GPT and for language understanding like BERT).
I have been connecting the dots between the capabilities of these models and their business application. I still believe we're in the beginning of grasping the amount of potential value we can extract from these models. Happy to get to share these as I learn them from my exposure to the problem space.
Some of the key visual language I'm aiming to simplify is that of "prompts" and their use to shape model output (leading to practical applications). In this post, a key visual is  which shows an example of a summarization prompt and  showing a high-level process of "prompt engineering".
Would appreciate your feedback!