That’s not a problem with RAG itself that’s an issue with your retriever. In the original RAG paper they used two vanilla BERT models and cosine similarity but there’s no requirement you do that. Use any retriever that gets you high precision. Use BM25 if you want, it’s simple and cheap.

You’re right in saying there’s not enough semantic meaning in the text of the query. The domain of queries and the domain of documents are very different. That’s why a real retrieval system will train the query encoder and doc encoder to be closer in their embedding space using click data. This is what Google is doing.