diff --git a/docs/Getting Started.ipynb b/docs/Getting Started.ipynb
index f73dfffe..8ab8e6a8 100644
--- a/docs/Getting Started.ipynb	
+++ b/docs/Getting Started.ipynb	
@@ -9,35 +9,33 @@
     "\n",
     "Here you will learn how to use the fastembed package to embed your data into a vector space. The package is designed to be easy to use and fast. It is built on top of the [ONNX](https://onnx.ai/) standard, which allows for fast inference on a variety of hardware (called Runtimes in ONNX). \n",
     "\n",
-    "## Installation\n",
+    "## Quick Start\n",
     "\n",
-    "To get started, install the fastembed package with pip."
+    "The fastembed package is designed to be easy to use. The main class is the `Embedding` class. It takes a list of strings as input and returns a list of vectors as output. The `Embedding` class is initialized with a model file."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a3b5612a",
+   "id": "ada95c6a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install fastembed"
+    "!pip install fastembed --upgrade # Install fastembed"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6b5b503b",
+   "id": "ed81d725",
    "metadata": {},
    "source": [
-    "# Quick Start\n",
-    "\n",
-    "The fastembed package is designed to be easy to use. The main class is the `Embedding` class. It takes a list of strings as input and returns a list of vectors as output. The `Embedding` class is initialized with a model file."
+    "Make the necessary imports, initialize the `Embedding` class, and embed your data into vectors:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8349e72d",
+   "id": "b61c6552",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +61,7 @@
    "id": "8c49ae50",
    "metadata": {},
    "source": [
-    "## Explanation of what is happening"
+    "## What is happening under the hood?"
    ]
   },
   {
@@ -93,10 +91,10 @@
    "source": [
     "Notice that we are using the FlagEmbedding -- which is state of the Art and beats OpenAI's Embedding by a large margin. \n",
     "\n",
-    "## Prepare your Documents\n",
+    "### Prepare your Documents\n",
     "You can define a list of documents that you'd like to encode. These can be sentences, paragraphs, or even entire documents. \n",
     "\n",
-    "### Format of the List:\n",
+    "#### Format of the List:\n",
     "1. List of Strings: Your documents must be in a list, and each document must be a string.\n",
     "2. For Retrieval Tasks: If you're working with queries and passages, you can add special labels to them:\n",
     "- **Queries**: Add \"query:\" at the beginning of each query string.\n",
@@ -125,7 +123,7 @@
    "id": "1cb3cc87",
    "metadata": {},
    "source": [
-    "## Load the Embedding Model Weights\n",
+    "### Load the Embedding Model Weights\n",
     "Next, initialize the Embedding class with the desired parameters. Here, \"BAAI/bge-small-en\" is the pre-trained model name, and max_length=512 is the maximum token length for each document."
    ]
   },
@@ -145,7 +143,7 @@
    "id": "5549d501",
    "metadata": {},
    "source": [
-    "## Embed your Documents\n",
+    "### Embed your Documents\n",
     "\n",
     "Use the encode method of the embedding model to transform the documents into a List of np.array. The method returns a generator, so we cast it to a list to get the embeddings."
    ]