IIUC they're doing some sort of "q/a" for each chunk from documents, where they ask an LLM to "play the user role and ask a question that would be answered by this chunk". They then embed those questions, and match live user queries with those questions first, then maybe re-rank on the document chunks retrieved.

I wonder if backwards a backward predicting LLM might do better.

E.g. augment the data with "Certainly, the document matching your question is <document>" and then sample the backwards completion to get candidate questions.