diff --git a/cookbook/pageindex_RAG_simple.ipynb b/cookbook/pageindex_RAG_simple.ipynb
index fa6400d..fed785c 100644
--- a/cookbook/pageindex_RAG_simple.ipynb
+++ b/cookbook/pageindex_RAG_simple.ipynb
@@ -39,13 +39,19 @@
         "\n",
         "- **No Vectors Needed**: Uses document structure and LLM reasoning for retrieval.\n",
         "- **No Chunking Needed**: Documents are organized into natural sections rather than artificial chunks.\n",
-        "- **No Top-K Needed**: The LLM decides how many nodes need to be retrieved.\n",
-        "- **Transparent Retrieval Process**: Retrieval based on reasoning — say goodbye to approximate semantic search ('vibe retrieval').\n",
+        "- **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents. \n",
+        "- **Transparent Retrieval Process**: Retrieval based on reasoning — say goodbye to approximate semantic search ('vibe retrieval')."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 📝 About this Notebook\n",
         "\n",
-        "# 📝  About this Notebook\n",
         "This notebook demonstrates a simple example of **vectorless RAG** with PageIndex. You will learn:\n",
-        "- [x] How to generate PageIndex tree structure of a document.\n",
-        "- [x] How to perform retrieval with tree search.\n",
+        "- [x] How to build a PageIndex tree structure of a document.\n",
+        "- [x] How to perform reasoning-based retrieval with tree search.\n",
         "- [x] How to generate the answer based on the retrieved context."
       ]
     },
@@ -55,7 +61,7 @@
         "id": "7ziuTbbWcG1L"
       },
       "source": [
-        "# Preparation\n",
+        "## Preparation\n",
         "\n"
       ]
     },
@@ -65,7 +71,7 @@
         "id": "edTfrizMFK4c"
       },
       "source": [
-        "## Install Dependencies"
+        "### Install dependencies"
       ]
     },
     {
@@ -86,7 +92,7 @@
         "id": "WVEWzPKGcG1M"
       },
       "source": [
-        "## Setup Environment"
+        "### Setup environment"
       ]
     },
     {
@@ -114,7 +120,7 @@
         "id": "AR7PLeVbcG1N"
       },
       "source": [
-        "## Define Utility Functions"
+        "### Define utility functions"
       ]
     },
     {
@@ -169,7 +175,7 @@
         "id": "heGtIMOVcG1N"
       },
       "source": [
-        "# Step 1: PageIndex Tree Generation"
+        "## Step 1: PageIndex Tree Generation"
       ]
     },
     {
@@ -178,7 +184,7 @@
         "id": "Mzd1VWjwMUJL"
       },
       "source": [
-        "## Submit a document with PageIndex SDK"
+        "### Submit a document with PageIndex SDK"
       ]
     },
     {
@@ -224,7 +230,7 @@
         "id": "4-Hrh0azcG1N"
       },
       "source": [
-        "## Get the generated PageIndex tree structure"
+        "### Get the generated PageIndex tree structure"
       ]
     },
     {
@@ -329,9 +335,9 @@
         "id": "USoCLOiQcG1O"
       },
       "source": [
-        "# Step 2: Reasoning-Based Retrieval with Tree Search\n",
+        "## Step 2: Reasoning-Based Retrieval with Tree Search\n",
         "\n",
-        "#### Use LLM to search the PageIndex tree and decide which nodes may contain the relevant context."
+        "### Use LLM for tree search and decide which nodes might contain relevant context"
       ]
     },
     {
@@ -367,6 +373,13 @@
         "tree_search_result = await call_llm(search_prompt)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Print retrieved nodes and reasoning process"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -426,8 +439,6 @@
         }
       ],
       "source": [
-        "### Print retrieval nodes\n",
-        "\n",
         "node_map = create_node_mapping(tree)\n",
         "tree_search_result_json = json.loads(tree_search_result)\n",
         "\n",
@@ -446,9 +457,9 @@
         "id": "10wOZDG_cG1O"
       },
       "source": [
-        "# Step 3: Answer Generation\n",
+        "## Step 3: Answer Generation\n",
         "\n",
-        "#### Extract context from relevant nodes and generate the final answer."
+        "### Extract relevant context from retrieved nodes"
       ]
     },
     {
@@ -496,12 +507,18 @@
         }
       ],
       "source": [
-        "# Prepare Retrieved Context\n",
-        "\n",
         "node_list = json.loads(tree_search_result)[\"node_list\"]\n",
         "relevant_content = \"\\n\\n\".join(node_map[node_id][\"text\"] for node_id in node_list)\n",
+        "\n",
         "print_markdown('## Retrieved Context', '---')\n",
-        "print_markdown(f'{relevant_content[:1000]} ...')"
+        "print_markdown(relevant_content[:1000] + ' ...')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate answer based on retrieved context"
       ]
     },
     {
@@ -548,8 +565,6 @@
         }
       ],
       "source": [
-        "# Generate Answer\n",
-        "\n",
         "answer_prompt = f\"\"\"\n",
         "Answer the question based on the context:\n",
         "\n",
@@ -572,15 +587,21 @@
       "source": [
         "# 🎯 What's Next\n",
         "\n",
-        "This notebook has demonstrated a basic example of **reasoning-based**, **vectorless** RAG with PageIndex. The workflow illustrates the core idea:\n",
-        "> *Generating a hierarchical tree structure from a document, reasoning over that tree structure, and extracting relevant context without relying on a vector database or top-k similarity search*.\n",
+        "This notebook has demonstrated a basic, minimal example of **reasoning-based**, **vectorless** RAG with PageIndex. The workflow illustrates the core idea:\n",
+        "> *Generating a hierarchical tree structure from a document, reasoning over that tree structure, and extracting relevant context, without relying on a vector database or top-k similarity search*.\n",
         "\n",
         "While this notebook highlights a minimal workflow, the PageIndex framework is built to support **far more advanced** use cases. In upcoming tutorials, we will introduce:\n",
-        "* **Multi-node reasoning for complex query** — Scale tree search to handle queries that require context from multiple nodes.\n",
-        "* **Multi-document search** — Enable reasoning-based navigation across large document collections, extending beyond a single file.\n",
-        "* **Efficient Tree search** — Improve tree search efficiency for long documents with a large number of nodes.\n",
+        "* **Multi-Node Reasoning with Content Extraction** — Scale tree search to extract and select relevant content from multiple nodes.\n",
+        "* **Multi-Document Search** — Enable reasoning-based navigation across large document collections, extending beyond a single file.\n",
+        "* **Efficient Tree Search** — Improve tree search efficiency for long documents with a large number of nodes.\n",
         "* **Expert Knowledge Integration and Preference Alignment** — Incorporate user preferences or expert insights by adding knowledge directly into the LLM tree search, without the need for fine-tuning.\n",
-        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
         "# 🔎 Learn More About PageIndex\n",
         "  <a href=\"https://vectify.ai\">🏠 Homepage</a>&nbsp; • &nbsp;\n",
         "  <a href=\"https://dash.pageindex.ai\">🖥️ Dashboard</a>&nbsp; • &nbsp;\n",
@@ -591,8 +612,7 @@
         "\n",
         "<br>\n",
         "\n",
-        "© 2025 [Vectify AI](https://vectify.ai)\n",
-        "\n"
+        "© 2025 [Vectify AI](https://vectify.ai)"
       ]
     }
   ],