diff --git a/parlai/agents/rag/retrievers.py b/parlai/agents/rag/retrievers.py
index bfff0fb77ba..5ec0aa596c3 100644
--- a/parlai/agents/rag/retrievers.py
+++ b/parlai/agents/rag/retrievers.py
@@ -641,12 +641,11 @@ def index_retrieve(
         # recompute exact FAISS scores
         scores = torch.bmm(query.unsqueeze(1), vectors.transpose(1, 2)).squeeze(1)
         if torch.isnan(scores).sum().item():
-            logging.error(
-                '\n[ Document scores are NaN; please look into the built index. ]\n'
-                '[ If using a compressed index, try building an exact index: ]\n'
-                '[ $ python index_dense_embeddings --indexer-type exact... ]'
+            raise AssertionError(
+                '\n[ Document scores are NaN; please make sure the passages file does not have repeated entries.]\n'
+                '[ Also set --num-shards to small values during generating dense embeddings: ]\n'
+                '[ e.g., when --shard-id is 0, --num-shards should be a value < int(len(rows of the passages file)/(num_of_maximum_passages_you_want_to_retrieve-1)).]'
             )
-            scores.fill_(1)
         ids = torch.tensor([[int(s) for s in ss] for ss in ids])
 
         return ids, scores