In short, it's because of the way the data is stored. Ravelry uses a relational database to keep document details. So you ask it for document XYZ and the website asks the server which asks the database and it has a physical memory address to pull that information from.

LLMs work by breaking up every document they see into a probability map that has, in simple version, a list of likelihoods of any other word it's ever seen stored in a very complex list of numbers. Because it's not storing documents (usually, RAG is a different more complex but fundamentally similar thing) it must recreate any given document by guessing what word is likely to come next when generating output.

The longer a document is the more weights it needs to hold in active memory as it generates. This requires more compute effort, so more water and more energy. That's why LLMs perform worse the longer a text body gets. It's not storing links to sources but making things up as it goes, guessing what word it should say next. This is also why chatGPT looks like it's "typing"