diff --git a/docs/docs/integrations/text_embedding/azureopenai.ipynb b/docs/docs/integrations/text_embedding/azureopenai.ipynb
index d484b2883a8..049335e0a6d 100644
--- a/docs/docs/integrations/text_embedding/azureopenai.ipynb
+++ b/docs/docs/integrations/text_embedding/azureopenai.ipynb
@@ -131,7 +131,7 @@
       "source": [
         "## Indexing and Retrieval\n",
         "\n",
-        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/rag).\n",
         "\n",
         "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
       ]