It's funny because things are finally coming full circle in ML.

10-15 years ago the challenge in ML/PR was "feature engineering", the careful crafting of rules that would define features in the data which would draw the attention of the ML algorithm.

Then deep learning came along and it solved the issue of feature engineering; just throw massive amounts of data at the problem and the ML algorithms can discern the features automatically, without having to craft them by hand.

Now we've gone as far as we can with massive data, and the problem seems to be that it's difficult to bring out the relevent details when there's so much data. Hence "context engineering", a manual, heuristic-heavy processes guided by trial and error and intuition. More an art than science. Pretty much the same thing that "feature engineering" was.