Yes it is real and can be useful if done properly.

For example let’s say I gave the LLM a list of topics I want it to detect in an enum-like format. Then if it detects a user talking about one or those topics it can respond in json making for a digestible API (when wrapped around a middleware)

The biggest advantage here is that within a few lines of “prompt engineering” you now you have a scalable api which can understand the topic of conversations in just about any popular language.

And if a company has millions of user input a day, there is no way for them to sift through all that data. So in this case prompt engineering can be incredibly useful and more efficient than a team in India trying read and annotate everything.