Is there any prior writing about finding under-sampled latent space in a model and directing that behavior into documentation writing?

I was fixing cache invalidation and this page was the right thing at the right time to help me understand the solution to the problem: https://grokipedia.com/page/Cache_busting_in_Vite#troubleshooting

AFAIK, that collection of information is a new synthesis of many different bits of documentation, and presented in a way that got me to understanding faster and more completely than reading the disparate threads.

As a mechanism for probing the model, is this not generalizable? Given my "truly novel integration of existing data" assumption, is there a way to successfully sample under-explored latent spaces of the model and get interpretable results once you bump the output into the "wikipedia writer" direction?

If you could "diff" a model to find where the weights changed the most during training/tuning, you could distill down what the model has learned in an interpretable format.