The combination of encoding / tokenization of meanings and ideas, related concepts, and mapping these relationships in vector space makes LLMs not so much glorified text prediction engines as browsers/oracles of the sum total of cultural-linguistic knowledge as captured in the training corpus.
Understanding how the implicit and explicit linguistic, memetic, and cultural context is integrated into the idea/concept/text prediction engine helps to show how LLMs produce such convincing output and why they often can bring useful information to the table.
More importantly, understanding this holistically can help people to predict where the output that LLMs can generate will -not- be particularly useful or even may be wildly misleading.