As the other comment mentions, you can paste the content of entire books or documents and ask very pointed question about it. Last year, Anthropic was showing off their 100K context window, and that's exactly what they did, they gave it the content of The Great Gatsby and asked it questions about specific lines of the book.
Similarly, imagine giving it hundreds of documents and asking it to spot some specific detail in there.
One point I'm unclear on is how these huge context sizes are implemented by the various models. Are any of them the actual raw "width of the model" that is propagated through it, or are these all hierarchical summarization and chunk embedding index lookup type tricks?
The Encyclopedia Britannica is ~44M words.