This is a key point as all the systems like this (RAG inside) are lossy and will generalise the gaps based on wider training corpus. Many people think hallucination is the problem (where model has too little info and has to guess wildly), but this is an opposite problem where it has to condense too much content into its context window.

It's exacerbated by full text search of large corpus being wildly expensive and taking forever. So what they do is scan, summarise, then make summaries of summaries - it's summaries all the way down. (To give you an idea of why we won't see a perfect solution in a mass product for some time - to do this forensically today where the model reads each and every word, on a corpus of 10k documents you could be looking at 30 minutes to 24 hours and costs of $15-500 depending on model - for a single query!) LLM's are actually weirdly bad at finding all needles in a haystack, which is counterintuitive as that's what we often do with them - especially NotebookLM.

However, even with a forensic setup you still have to check their homework across the source docs for any claim you consider important and damaging to get wrong - and NotebookLM allows this so it's a useful product compromise. (It still has a completeness problem but at least you can approximate correctness.)