diff --git a/examples/demo_basic.ipynb b/examples/demo_basic.ipynb
index f7baf9d..9802d9d 100644
--- a/examples/demo_basic.ipynb
+++ b/examples/demo_basic.ipynb
@@ -4,7 +4,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# LlamaExtract Usage"
+    "# Infer a schema to extract data from files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we will demonstrate how to infer a schema from a set of files and using it to extract structured data from invoice PDF files.\n",
+    "\n",
+    "The steps are:\n",
+    "1. Infer a schema from the invoices files.\n",
+    "2. Extract structured data (i.e. JSONs) from invoice PDF files\n",
+    "\n",
+    "Additional Resources:\n",
+    "- `LlamaExtract`: https://docs.cloud.llamaindex.ai/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "Install `llama-extract` client library:"
    ]
   },
   {
@@ -16,6 +38,13 @@
     "%pip install llama-extract"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apply `nest_asyncio` and bring your own LlamaCloud API key:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -32,6 +61,14 @@
     "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-...\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Infer the schema\n",
+    "First, let's infer the schema using the invoice files with `LlamaExtract`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -47,6 +84,13 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preview the inferred schema:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -64,6 +108,14 @@
     "print(extraction_schema.data_schema)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract structured data\n",
+    "Now with the schema, we can extract structured data (i.e. JSON) from the our invoices files."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -84,6 +136,13 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preview the extracted data:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/examples/demo_existent_schema.ipynb b/examples/demo_existent_schema.ipynb
deleted file mode 100644
index 3f1596e..0000000
--- a/examples/demo_existent_schema.ipynb
+++ /dev/null
@@ -1,123 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extracting data from files using an existing schema"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install llama-extract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# llama-extract is async-first, running the sync code in a notebook requires the use of nest_asyncio\n",
-    "import nest_asyncio\n",
-    "\n",
-    "nest_asyncio.apply()\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-...\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_extract import LlamaExtract\n",
-    "\n",
-    "extractor = LlamaExtract()\n",
-    "\n",
-    "extraction_schema = extractor.get_schema(\"schema_id...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "id='88ea0633-937b-42f1-a35d-7da19c2db74e' created_at=datetime.datetime(2024, 7, 24, 19, 48, 49, 968786, tzinfo=datetime.timezone.utc) updated_at=datetime.datetime(2024, 7, 24, 19, 48, 49, 968786, tzinfo=datetime.timezone.utc) name='Test Schema' project_id='b1be5ffd-3f90-4fd1-9742-ca7c0a30f6f7' data_schema={'type': 'object', 'properties': {'date': {'type': 'string'}, 'amount': {'type': 'number'}, 'number': {'type': 'string'}}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(extraction_schema)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Extracting files: 100%|██████████| 2/2 [00:03<00:00,  1.71s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "extractions = extractor.extract(\n",
-    "    extraction_schema.id,\n",
-    "    [\"./data/noisebridge_receipt.pdf\", \"./data/parallels_invoice.pdf\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'date': 'Jul 23, 2024', 'amount': '119.99', 'number': 'BKD-73649835575'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(extractions[1].data)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llama-extract-tm5usU00-py3.11",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/demo_manual.ipynb b/examples/demo_manual.ipynb
index b2db5f1..3f657c2 100644
--- a/examples/demo_manual.ipynb
+++ b/examples/demo_manual.ipynb
@@ -4,7 +4,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Create a schema with your own schema to extract data from files"
+    "# Manually create a schema to extract data from files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we will demonstrate how to manually create a schema and using it to extract structured data from invoice PDF files.\n",
+    "\n",
+    "The steps are:\n",
+    "1. Create a schema using a valid JSON schema object.\n",
+    "2. Extract structured data (i.e. JSONs) from invoice PDF files\n",
+    "\n",
+    "Additional Resources:\n",
+    "- `LlamaExtract`: https://docs.cloud.llamaindex.ai/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "Install `llama-extract` client library:"
    ]
   },
   {
@@ -16,6 +38,13 @@
     "%pip install llama-extract"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apply `nest_asyncio` and bring your own LlamaCloud API key:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -32,6 +61,14 @@
     "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-...\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the schema\n",
+    "First, let's create the schema using a valid JSON schema object with `LlamaExtract`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -54,6 +91,13 @@
     "extraction_schema = extractor.create_schema(\"Test Schema\", data_schema)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's preview the created schema:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -71,6 +115,14 @@
     "print(extraction_schema)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract structured data\n",
+    "Now with the schema, we can extract structured data (i.e. JSON) from the our invoices files."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -91,6 +143,13 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preview the extracted data:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,