diff --git a/docs/my-website/docs/providers/azure_ai.md b/docs/my-website/docs/providers/azure_ai.md
index 16ec15b1007..b8dbe16ba9e 100644
--- a/docs/my-website/docs/providers/azure_ai.md
+++ b/docs/my-website/docs/providers/azure_ai.md
@@ -1,13 +1,21 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
 # Azure AI Studio
 
 ## Sample Usage
-The `azure/` prefix sends this to Azure
 
-Ensure you add `/v1` to your api_base. Your Azure AI studio `api_base` passed to litellm should look something like this
-```python
-api_base = "https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com/v1/"
-```
+**Ensure the following:**
+1. The API Base passed ends in the `/v1/` prefix
+  example:
+  ```python
+  api_base = "https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com/v1/"
+  ```
 
+2. The `model` passed is listed in [supported models](#supported-models). You **DO NOT** Need to pass your deployment name to litellm. Example `model=azure/Mistral-large-nmefg`  
+
+
+**Quick Start**
 ```python
 import litellm
 response = litellm.completion(
@@ -20,28 +28,83 @@ response = litellm.completion(
 
 ## Sample Usage - LiteLLM Proxy
 
-Set this on your litellm proxy config.yaml
-```yaml
-model_list:
-  - model_name: mistral
-    litellm_params:
-      model: mistral/Mistral-large-dfgfj
-      api_base: https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com/v1/
-      api_key: JGbKodRcTp****
-  - model_name: command-r-plus
-    litellm_params:
-        model: azure/command-r-plus
-        api_key: os.environ/AZURE_COHERE_API_KEY
-        api_base: os.environ/AZURE_COHERE_API_BASE
-```
+1. Add models to your config.yaml
+
+  ```yaml
+  model_list:
+    - model_name: mistral
+      litellm_params:
+        model: azure/mistral-large-latest
+        api_base: https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com/v1/
+        api_key: JGbKodRcTp****
+    - model_name: command-r-plus
+      litellm_params:
+          model: azure/command-r-plus
+          api_key: os.environ/AZURE_COHERE_API_KEY
+          api_base: os.environ/AZURE_COHERE_API_BASE
+  ```
+
+
+
+2. Start the proxy 
+
+  ```bash
+  $ litellm --config /path/to/config.yaml
+  ```
+
+3. Send Request to LiteLLM Proxy Server
+
+  <Tabs>
+
+  <TabItem value="openai" label="OpenAI Python v1.0.0+">
+
+  ```python
+  import openai
+  client = openai.OpenAI(
+      api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
+      base_url="http://0.0.0.0:4000" # litellm-proxy-base url
+  )
+
+  response = client.chat.completions.create(
+      model="mistral",
+      messages = [
+          {
+              "role": "user",
+              "content": "what llm are you"
+          }
+      ],
+  )
+
+  print(response)
+  ```
+  </TabItem>
+
+  <TabItem value="curl" label="curl">
+
+  ```shell
+  curl --location 'http://0.0.0.0:4000/chat/completions' \
+      --header 'Authorization: Bearer sk-1234' \
+      --header 'Content-Type: application/json' \
+      --data '{
+      "model": "mistral",
+      "messages": [
+          {
+          "role": "user",
+          "content": "what llm are you"
+          }
+      ],
+  }'
+  ```
+  </TabItem>
+
+  </Tabs>
 
 ## Supported Models
 
 | Model Name               | Function Call                                                                                                                                                      |
 |--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| command-r-plus | `completion(model="azure/command-r-plus", messages)` | 
-| command-r | `completion(model="azure/command-r", messages)` | 
+| Cohere command-r-plus | `completion(model="azure/command-r-plus", messages)` | 
+| Cohere ommand-r | `completion(model="azure/command-r", messages)` | 
 | mistral-large-latest | `completion(model="azure/mistral-large-latest", messages)` | 
 
 
-