Your retriever returned the right documents. The similarity scores look fine. The answer is still wrong. If you've shipped RAG, you've seen this — and it's the failure that survives every retrieval upgrade.
What everyone tries
Reranker. Higher top-k. Hybrid search. A better embedding model. All of these chase the same goal: documents more similar to the query. They help when the right document wasn't being retrieved. They do nothing when the right document was retrieved and the answer is still wrong.
Why it doesn't work
Similarity answers "is this chunk about the same topic?" It does not answer "does this chunk contain the facts needed to support the answer?" Those come apart constantly. A chunk can be highly similar — same vocabulary, same subject — and contain nothing that actually grounds the answer. Hand the model a pile of on-topic text and it will produce a fluent, plausible, even cited-looking answer. The grounding is cosmetic: the text was nearby, not load-bearing.
