I'm in the drug discovery space, and for all the "engineering biology" talk that's around these days, I find a bunch of parallels to drug discovery in the AI, particularly LLM space.<p>The fact that these models get created and the poked and prodded with prompts reminds me a ton of chemical biology, aka the field of using chemicals to investigate biology.<p>It would appear that in the case of AI, the engineering has become art.<p>Ironically these technologies are the ones being heralded as the way to take biology and turn it into an engineering problem.<p>I always come back to may favorite XKCD "Here to Help"
https://xkcd.com/1831
I think the naming originates from the similar term "feature engineering" which had been in use for about as long as machine learning in general. The approaches to feature engineering are also a bit of a mix of science and art -- in some cases statistical and probability theory are used to arrive at an appropriate feature mix, in other cases it's very much throwing everything at the wall to measure stickiness.<p>Prompt engineering is definitely a bit more art than engineering, but it's not far off conceptually from what feature engineering is.
Also have a background in systems biology here, originally a chemical engineer. Used to do "sensitivity analysis" and parameter fitting on a bunch of PK models for pharma labs. Drifted to ML and now in the LLM space.<p>Prompt engineering - indeed a glorified way of plugging in values and see where comes out/where it takes you. I remember I did not enjoy guess-timating parameter bounds on a global search, and couldn't "configure" simulations as well as my experienced colleague. But well, here I am.