AFAIK a good way to provide better answers and avoid hallucinations would be to compute embeddings for all sections of text in Wikipedia and then when a user asks a question create an embedding from that question.

Use it to find the X closest embeddings to the question being posed, lookup their original articles, feed them all into context of an LLM and then ask it to answer the question based on that context (alone).

Contexts are becomming quite large so it's possible to put a lot of stuff in there. LLMs answering questions based on a giben text seem to be more reliable than those that are simply trained/fine tuned on some library of texts. p