Query expansion works better.

Query expansion and re ranking can and often do coexist

Roughly, first there is the query analysis/manipulation phase where you might have NER, spell check, query expansion/relaxation etc

Then there is the selection phase, where you retrieve all items that are relevant. Sometimes people will bring in results from both text and vector based indices. Perhaps and additional layer to group results

Then finally you have the reranking layer using a cross encoder model which might even have some personalisation in the mix

Also, with vector search you might not need query expansion necessarily since semantic similarity does loose association. But every domain is unique and there’s only one way to find out

Query expansion happens before the retrieval query, reranking is applied after the ranked results are returned, both are important