hmmm, I can't speak to people using word2vec in conjunction with RAG, but the other use case is actually pretty common. (you don't need to generate answers though in my experience).
For each document intended for ingestion into a vector database:
- Use an LLM to generate a list of possible questions that the document is capable of answering (essentially equivalent to generating a quiz)
- Map these question embeddings back to the original documents
- Store document, document chunks, question 1, question 2, etc. into the vector database
So now when a person queries your RAG, you have the direct link from user query -> doc chunks, but additionally the transitionary link from user query -> similar query -> doc chunk.
For each document intended for ingestion into a vector database:
- Use an LLM to generate a list of possible questions that the document is capable of answering (essentially equivalent to generating a quiz)
- Map these question embeddings back to the original documents
- Store document, document chunks, question 1, question 2, etc. into the vector database
So now when a person queries your RAG, you have the direct link from user query -> doc chunks, but additionally the transitionary link from user query -> similar query -> doc chunk.