I can't answer for the kindly poster above (ty), but from our experience techniques like HyDE are great when you are getting a lot of comparative questions.
For instance, if a user asks "How does A compare to B" then the query expansion element of HyDE is incredibly useful. The actual value of translating queries into answers for embedding is a bit unclear, since most embedding models we are using have been ft'ed to map queries onto answers.
Not GP but Hyde is a crutch for having poor semantic indexing imho. Most people just take raw chunks and embed those. You really need a robust preprocessing pipeline.
For instance, if a user asks "How does A compare to B" then the query expansion element of HyDE is incredibly useful. The actual value of translating queries into answers for embedding is a bit unclear, since most embedding models we are using have been ft'ed to map queries onto answers.