diff --git a/docs/extras/use_cases/question_answering/question_answering.ipynb b/docs/extras/use_cases/question_answering/question_answering.ipynb
index 5eda2bd40ae..73513ca60a4 100644
--- a/docs/extras/use_cases/question_answering/question_answering.ipynb
+++ b/docs/extras/use_cases/question_answering/question_answering.ipynb
@@ -13,7 +13,7 @@
     "Suppose you have some text documents (PDF, blog, Notion pages, etc.) and want to ask questions related to the contents of those documents. LLMs, given their proficiency in understanding text, are a great tool for this.\n",
     "\n",
     "In this walkthrough we'll go over how to build a question-answering over documents application using LLMs. Two very related use cases which we cover elsewhere are:\n",
-    "- [QA over structured data](/docs/use_cases/sql) (e.g., SQL)\n",
+    "- [QA over structured data](/docs/use_cases/qa_structured/sql) (e.g., SQL)\n",
     "- [QA over code](/docs/use_cases/code_understanding) (e.g., Python)\n",
     "\n",
     "![intro.png](/img/qa_intro.png)\n",
@@ -620,7 +620,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,