diff --git a/supporting-blog-content/fetch-surrounding-chunks/fetch-surrounding-chunks.ipynb b/supporting-blog-content/fetch-surrounding-chunks/fetch-surrounding-chunks.ipynb
index 055fbe7d..128d8363 100644
--- a/supporting-blog-content/fetch-surrounding-chunks/fetch-surrounding-chunks.ipynb
+++ b/supporting-blog-content/fetch-surrounding-chunks/fetch-surrounding-chunks.ipynb
@@ -204,9 +204,9 @@
     "## Import model\n",
     "Using the eland_import_hub_model script, download and install all-MiniLM-L6-v2 transformer model. Setting the NLP --task-type as text_embedding.\n",
     "\n",
-    "To get the cloud id, go to Elastic cloud and On the deployment overview page, copy down the Cloud ID.\n",
+    "To get the Cloud ID, go to Elastic cloud and on the deployment overview page, copy down the Cloud ID.\n",
     "\n",
-    "To authenticate your request, You could use API key. Alternatively, you can use your cloud deployment username and password."
+    "To authenticate your request, you could use API key. Alternatively, you can use your cloud deployment username and password."
    ],
    "metadata": {
     "id": "rOWheQ-uJE2C"
@@ -277,9 +277,9 @@
     "# delete model if already downloaded and deployed\n",
     "try:\n",
     "    esclient.ml.delete_trained_model(model_id=elser_model_id, force=True)\n",
-    "    print(\"Model deleted successfully, We will proceed with creating one\")\n",
+    "    print(\"Model deleted successfully, we will proceed with creating one\")\n",
     "except exceptions.NotFoundError:\n",
-    "    print(\"Model doesn't exist, but We will proceed with creating one\")\n",
+    "    print(\"Model doesn't exist, but we will proceed with creating one\")\n",
     "\n",
     "# Creates the ELSER model configuration. Automatically downloads the model if it doesn't exist.\n",
     "esclient.ml.put_trained_model(\n",
@@ -310,10 +310,10 @@
     "    )\n",
     "\n",
     "    if status[\"trained_model_configs\"][0][\"fully_defined\"]:\n",
-    "        print(\"ELSER Model is downloaded and ready to be deployed.\")\n",
+    "        print(\"ELSER model is downloaded and ready to be deployed.\")\n",
     "        break\n",
     "    else:\n",
-    "        print(\"ELSER Model is downloaded but not ready to be deployed.\")\n",
+    "        print(\"ELSER model is downloaded but not ready to be deployed.\")\n",
     "    time.sleep(5)"
    ],
    "metadata": {
@@ -346,10 +346,10 @@
     "        model_id=elser_model_id,\n",
     "    )\n",
     "    if status[\"trained_model_stats\"][0][\"deployment_stats\"][\"state\"] == \"started\":\n",
-    "        print(\"ELSER Model has been successfully deployed.\")\n",
+    "        print(\"ELSER model has been successfully deployed.\")\n",
     "        break\n",
     "    else:\n",
-    "        print(\"ELSER Model is currently being deployed.\")\n",
+    "        print(\"ELSER model is currently being deployed.\")\n",
     "    time.sleep(5)"
    ],
    "metadata": {
@@ -697,9 +697,9 @@
     "                for nested_hit in nested_hits:\n",
     "                    chunk_number = nested_hit[\"_source\"][\"chunk_number\"]\n",
     "                    text = nested_hit[\"_source\"][\"text\"]\n",
-    "                    # print(f\"Text from Chunk {chunk_number}: {text}\")\n",
+    "                    # print(f\"Text from chunk {chunk_number}: {text}\")\n",
     "                    print(\n",
-    "                        f\"\\n\\nText from Chunk {chunk_number}: {textwrap.fill(first_passage_text, width=200)}\"\n",
+    "                        f\"\\n\\nText from chunk {chunk_number}: {textwrap.fill(first_passage_text, width=200)}\"\n",
     "                    )\n",
     "    else:\n",
     "        print(\"No hits found.\")\n",
@@ -966,7 +966,7 @@
    "source": [
     "## Fetch and Process the Book Text\n",
     "\n",
-    "This section downloads the full text of \"Harry Potter and the Sorcerer's Stone\" from a specified URL and processes it to extract chapters and their titles. The text is then structured into a pandas DataFrame for further analysis and indexing.\n",
+    "This section downloads the full text of \"Harry Potter and the Sorcerer's Stone\" from a specified URL and processes it to extract chapters and their titles. The text is then structured into a Pandas DataFrame for further analysis and indexing.\n",
     "\n",
     "### Key Steps:\n",
     "1. **Download Text**: The book is fetched using `urllib.request` from the provided URL.\n",
@@ -1052,7 +1052,7 @@
    "source": [
     "## Indexing DataFrame into Elasticsearch\n",
     "\n",
-    "This section uploads the structured data from a pandas DataFrame into a specified Elasticsearch index. The DataFrame contains chapter information from \"Harry Potter and the Sorcerer's Stone\", including chapter titles, full texts, and additional metadata.\n",
+    "This section uploads the structured data from a Pandas DataFrame into a specified Elasticsearch index. The DataFrame contains chapter information from \"Harry Potter and the Sorcerer's Stone\", including chapter titles, full texts, and additional metadata.\n",
     "\n",
     "### Key Operation:\n",
     "- **Index Data**: The `index_dataframe` function is called with the Elasticsearch client, the raw source index name, and the DataFrame as arguments. This operation effectively uploads the data into Elasticsearch, making it searchable and ready for further processing.\n"
@@ -1318,4 +1318,4 @@
    ]
   }
  ]
-}
\ No newline at end of file
+}