diff --git a/docs/examples/rag_weaviate.ipynb b/docs/examples/rag_weaviate.ipynb
index 627e8927..52ad11cb 100644
--- a/docs/examples/rag_weaviate.ipynb
+++ b/docs/examples/rag_weaviate.ipynb
@@ -43,7 +43,7 @@
     "\n",
     "Note: For best results, please use **GPU acceleration** to run this notebook. Here are two options for running this notebook:\n",
     "1. **Locally on a MacBook with an Apple Silicon chip.** Converting all documents in the notebook takes ~2 minutes on a MacBook M2 due to Docling's usage of MPS accelerators.\n",
-    "2. **Run this notebook on Google Colab.** Converting all documents in the notebook takes ~8 mintutes on a Google Colab T4 GPU."
+    "2. **Run this notebook on Google Colab.** Converting all documents in the notebook takes ~8 minutes on a Google Colab T4 GPU."
    ]
   },
   {
@@ -716,7 +716,7 @@
     "id": "7tGz49nfUegG"
    },
    "source": [
-    "We can see that our RAG pipeline performs relatively well for simple queries, especially given the small size of the dataset. Scaling this method for converting a larger sample of PDFs would require more compute (GPUs) and a more advanced deployment of Weaviate (like Docker, Kubernetes, or Weaviate Cloud). For more information on available Weaviate configurations, check out the [documetation](https://weaviate.io/developers/weaviate/starter-guides/which-weaviate)."
+    "We can see that our RAG pipeline performs relatively well for simple queries, especially given the small size of the dataset. Scaling this method for converting a larger sample of PDFs would require more compute (GPUs) and a more advanced deployment of Weaviate (like Docker, Kubernetes, or Weaviate Cloud). For more information on available Weaviate configurations, check out the [documentation](https://weaviate.io/developers/weaviate/starter-guides/which-weaviate)."
    ]
   }
  ],