diff --git a/README.md b/README.md
index fa53b28a..0aed36bf 100644
--- a/README.md
+++ b/README.md
@@ -52,6 +52,7 @@ Most code examples are written in Python, though the concepts can be applied in
 - DALL-E
   - [How to generate and edit images with DALL-E](examples/dalle/Image_generations_edits_and_variations_with_DALL-E.ipynb)
 - Azure OpenAI (alternative API from Microsoft Azure)
+  - [How to use ChatGPT with Azure OpenAI](examples/azure/chat.ipynb)
   - [How to get completions from Azure OpenAI](examples/azure/completions.ipynb)
   - [How to get embeddings from Azure OpenAI](examples/azure/embeddings.ipynb)
   - [How to fine-tune GPT-3 with Azure OpenAI](examples/azure/finetuning.ipynb)
diff --git a/examples/azure/chat.ipynb b/examples/azure/chat.ipynb
new file mode 100644
index 00000000..e3965c95
--- /dev/null
+++ b/examples/azure/chat.ipynb
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Azure chat completions example (preview)\n",
+    "In this example we'll try to go over all operations needed to get chat completions working using the Azure endpoints. \\\n",
+    "This example focuses on chat completions but also touches on some other operations that are also available using the API. This example is meant to be a quick way of showing simple operations and is not meant as a tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "For the following sections to work properly we first have to setup some things. Let's start with the `api_base` and `api_version`. To find your `api_base` go to https://portal.azure.com, find your resource and then under \"Resource Management\" -> \"Keys and Endpoints\" look for the \"Endpoint\" value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai.api_version = '2023-03-15-preview'\n",
+    "openai.api_base = '' # Please add your endpoint here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We next have to setup the `api_type` and `api_key`. We can either get the key from the portal or we can get it through Microsoft Active Directory Authentication. Depending on this the `api_type` is either `azure` or `azure_ad`."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup: Portal\n",
+    "Let's first look at getting the key from the portal. Go to https://portal.azure.com, find your resource and then under \"Resource Management\" -> \"Keys and Endpoints\" look for one of the \"Keys\" values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai.api_type = 'azure'\n",
+    "openai.api_key = ''  # Please add your api key here"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (Optional) Setup: Microsoft Active Directory Authentication\n",
+    "Let's now see how we can get a key via Microsoft Active Directory Authentication. Uncomment the following code if you want to use Active Directory Authentication instead of keys from the portal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from azure.identity import DefaultAzureCredential\n",
+    "\n",
+    "# default_credential = DefaultAzureCredential()\n",
+    "# token = default_credential.get_token(\"https://cognitiveservices.azure.com/.default\")\n",
+    "\n",
+    "# openai.api_type = 'azure_ad'\n",
+    "# openai.api_key = token.token"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deployments\n",
+    "In this section we are going to create a deployment using the `gpt-35-turbo` model that we can then use to create chat completions."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deployments: Create manually\n",
+    "Let's create a deployment using the `gpt-35-turbo` model. Go to https://portal.azure.com, find your resource and then under \"Resource Management\" -> \"Model deployments\" create a new `gpt-35-turbo` deployment. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "deployment_id = \"\" # Fill in the deployment id from the portal here"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (Optional) Deployments: Create programatically\n",
+    "We can also create the deployment using code. Note that you can only create one deployment per model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = \"gpt-35-turbo\"\n",
+    "\n",
+    "# Now let's create the deployment\n",
+    "print(f'Creating a new deployment with model: {model}')\n",
+    "result = openai.Deployment.create(model=model, scale_settings={\"scale_type\":\"standard\"})\n",
+    "deployment_id = result[\"id\"]\n",
+    "print(f'Successfully created deployment with id: {deployment_id}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (Optional) Deployments: Wait for deployment to succeed\n",
+    "Now let's check the status of the newly created deployment and wait till it is succeeded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f'Checking for deployment status.')\n",
+    "resp = openai.Deployment.retrieve(id=deployment_id)\n",
+    "status = resp[\"status\"]\n",
+    "print(f'Deployment {deployment_id} has status: {status}')\n",
+    "while status not in [\"succeeded\", \"failed\"]:\n",
+    "    resp = openai.Deployment.retrieve(id=deployment_id)\n",
+    "    status = resp[\"status\"]\n",
+    "    print(f'Deployment {deployment_id} has status: {status}')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create chat completion\n",
+    "Now let's send a sample chat completion to the deployment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For all possible arguments see https://platform.openai.com/docs/api-reference/chat-completions/create\n",
+    "response = openai.ChatCompletion.create(\n",
+    "    deployment_id=deployment_id,\n",
+    "    messages=[\n",
+    "        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
+    "        {\"role\": \"user\", \"content\": \"Knock knock.\"},\n",
+    "        {\"role\": \"assistant\", \"content\": \"Who's there?\"},\n",
+    "        {\"role\": \"user\", \"content\": \"Orange.\"},\n",
+    "    ],\n",
+    "    temperature=0,\n",
+    ")\n",
+    "\n",
+    "print(f\"{response.choices[0].message.role}: {response.choices[0].message.content}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also stream the response.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = openai.ChatCompletion.create(\n",
+    "    deployment_id=deployment_id,\n",
+    "    messages=[\n",
+    "        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
+    "        {\"role\": \"user\", \"content\": \"Knock knock.\"},\n",
+    "        {\"role\": \"assistant\", \"content\": \"Who's there?\"},\n",
+    "        {\"role\": \"user\", \"content\": \"Orange.\"},\n",
+    "    ],\n",
+    "    temperature=0,\n",
+    "    stream=True\n",
+    ")\n",
+    "\n",
+    "for chunk in response:\n",
+    "    delta = chunk.choices[0].delta\n",
+    "\n",
+    "    if \"role\" in delta.keys():\n",
+    "        print(delta.role + \": \", end=\"\", flush=True)\n",
+    "    if \"content\" in delta.keys():\n",
+    "        print(delta.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (Optional) Deployments: Delete\n",
+    "Finally let's delete the deployment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f'Deleting deployment: {deployment_id}')\n",
+    "openai.Deployment.delete(sid=deployment_id)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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",
+   "version": "3.8.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/azure/completions.ipynb b/examples/azure/completions.ipynb
index 53d42f42..217af9b6 100644
--- a/examples/azure/completions.ipynb
+++ b/examples/azure/completions.ipynb
@@ -11,12 +11,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import openai\n",
-    "from openai import cli"
+    "import openai"
    ]
   },
   {
@@ -29,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +206,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.8.10"
   },
   "vscode": {
    "interpreter": {