diff --git a/notebooks/2-Advanced_Getting_Started.ipynb b/notebooks/2-Advanced_Getting_Started.ipynb
index 2b76000..21bb020 100644
--- a/notebooks/2-Advanced_Getting_Started.ipynb
+++ b/notebooks/2-Advanced_Getting_Started.ipynb
@@ -61,18 +61,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
     "import getpass\n",
     "import json\n",
-    "import os\n",
     "import sys\n",
     "import time\n",
     "from pathlib import Path\n",
-    "from zipfile import ZipFile\n",
     "\n",
     "import magic\n",
     "import pandas as pd\n",
@@ -102,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "7",
    "metadata": {
     "tags": []
@@ -134,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "9",
    "metadata": {
     "tags": []
@@ -161,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "10",
    "metadata": {},
    "outputs": [],
@@ -183,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "12",
    "metadata": {
     "tags": []
@@ -274,28 +272,24 @@
     "def build_index(\n",
     "    storage_name: str,\n",
     "    index_name: str,\n",
-    "    entity_extraction_prompt_filepath: str = None,\n",
-    "    community_prompt_filepath: str = None,\n",
-    "    summarize_description_prompt_filepath: str = None,\n",
+    "    entity_extraction_prompt: str = None,\n",
+    "    entity_summarization_prompt: str = None,\n",
+    "    community_summarization_prompt: str = None,\n",
     ") -> requests.Response:\n",
-    "    \"\"\"Create a search index.\n",
-    "    This function kicks off a job that builds a knowledge graph (KG) index from files located in a blob storage container.\n",
+    "    \"\"\"Build a graphrag index.\n",
+    "    This function submits a job that builds a graphrag index (i.e. a knowledge graph) from data files located in a blob storage container.\n",
     "    \"\"\"\n",
     "    url = endpoint + \"/index\"\n",
-    "    prompt_files = dict()\n",
-    "    if entity_extraction_prompt_filepath:\n",
-    "        prompt_files[\"entity_extraction_prompt\"] = open(\n",
-    "            entity_extraction_prompt_filepath, \"r\"\n",
-    "        )\n",
-    "    if community_prompt_filepath:\n",
-    "        prompt_files[\"community_report_prompt\"] = open(community_prompt_filepath, \"r\")\n",
-    "    if summarize_description_prompt_filepath:\n",
-    "        prompt_files[\"summarize_descriptions_prompt\"] = open(\n",
-    "            summarize_description_prompt_filepath, \"r\"\n",
-    "        )\n",
+    "    prompts = dict()\n",
+    "    if entity_extraction_prompt:\n",
+    "        prompts[\"entity_extraction_prompt\"] = entity_extraction_prompt\n",
+    "    if entity_summarization_prompt:\n",
+    "        prompts[\"summarize_descriptions_prompt\"] = entity_summarization_prompt\n",
+    "    if community_summarization_prompt:\n",
+    "        prompts[\"community_report_prompt\"] = community_summarization_prompt\n",
     "    return requests.post(\n",
     "        url,\n",
-    "        files=prompt_files if len(prompt_files) > 0 else None,\n",
+    "        files=prompts if len(prompts) > 0 else None,\n",
     "        params={\"index_name\": index_name, \"storage_name\": storage_name},\n",
     "        headers=headers,\n",
     "    )\n",
@@ -475,15 +469,11 @@
     "        return response\n",
     "\n",
     "\n",
-    "def generate_prompts(storage_name: str, zip_file_name: str, limit: int = 1) -> None:\n",
+    "def generate_prompts(storage_name: str, limit: int = 1) -> None:\n",
     "    \"\"\"Generate graphrag prompts using data provided in a specific storage container.\"\"\"\n",
     "    url = endpoint + \"/index/config/prompts\"\n",
     "    params = {\"storage_name\": storage_name, \"limit\": limit}\n",
-    "    with requests.get(url, params=params, headers=headers, stream=True) as r:\n",
-    "        r.raise_for_status()\n",
-    "        with open(zip_file_name, \"wb\") as f:\n",
-    "            for chunk in r.iter_content():\n",
-    "                f.write(chunk)"
+    "    return requests.get(url, params=params, headers=headers)"
    ]
   },
   {
@@ -573,14 +563,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
-    "generate_prompts(storage_name=storage_name, limit=1, zip_file_name=\"prompts.zip\")\n",
-    "with ZipFile(\"prompts.zip\", \"r\") as zip_ref:\n",
-    "    zip_ref.extractall()"
+    "auto_template_response = generate_prompts(storage_name=storage_name, limit=1)\n",
+    "if auto_template_response.ok:\n",
+    "    prompts = auto_template_response.json()"
    ]
   },
   {
@@ -618,30 +608,20 @@
    },
    "outputs": [],
    "source": [
-    "# check if prompt files exist\n",
-    "entity_extraction_prompt_filepath = \"prompts/entity_extraction.txt\"\n",
-    "community_prompt_filepath = \"prompts/community_report.txt\"\n",
-    "summarize_description_prompt_filepath = \"prompts/summarize_descriptions.txt\"\n",
-    "entity_prompt = (\n",
-    "    entity_extraction_prompt_filepath\n",
-    "    if os.path.isfile(entity_extraction_prompt_filepath)\n",
-    "    else None\n",
-    ")\n",
-    "community_prompt = (\n",
-    "    community_prompt_filepath if os.path.isfile(community_prompt_filepath) else None\n",
-    ")\n",
-    "summarize_prompt = (\n",
-    "    summarize_description_prompt_filepath\n",
-    "    if os.path.isfile(summarize_description_prompt_filepath)\n",
-    "    else None\n",
-    ")\n",
+    "# check if custom prompts were generated\n",
+    "if \"auto_template_response\" in locals() and auto_template_response.ok:\n",
+    "    entity_extraction_prompt = prompts[\"entity_extraction_prompt\"]\n",
+    "    community_summarization_prompt = prompts[\"community_summarization_prompt\"]\n",
+    "    summarize_description_prompt = prompts[\"entity_summarization_prompt\"]\n",
+    "else:\n",
+    "    entity_extraction_prompt = community_summarization_prompt = summarize_description_prompt = None\n",
     "\n",
     "response = build_index(\n",
     "    storage_name=storage_name,\n",
     "    index_name=index_name,\n",
-    "    entity_extraction_prompt_filepath=entity_prompt,\n",
-    "    community_prompt_filepath=community_prompt,\n",
-    "    summarize_description_prompt_filepath=summarize_prompt,\n",
+    "    entity_extraction_prompt=entity_extraction_prompt,\n",
+    "    community_summarization_prompt=community_summarization_prompt,\n",
+    "    entity_summarization_prompt=summarize_description_prompt,\n",
     ")\n",
     "if response.ok:\n",
     "    pprint(response.json())\n",
@@ -717,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "31",
    "metadata": {},
    "outputs": [],