diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md
index adc723f67..9a01fa470 100644
--- a/docs/my-website/docs/providers/openai.md
+++ b/docs/my-website/docs/providers/openai.md
@@ -98,6 +98,62 @@ response = completion(
 | babbage-002         | `response = completion(model="babbage-002", messages=messages)` |
 | davinci-002         | `response = completion(model="davinci-002", messages=messages)` |
 
+## Advanced
+
+### Parallel Function calling
+See a detailed walthrough of parallel function calling with litellm [here](https://docs.litellm.ai/docs/completion/function_call)
+```python
+import litellm
+import json
+# set openai api key
+import os
+os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request
+# Example dummy function hard coded to return the same weather
+# In production, this could be your backend API or an external API
+def get_current_weather(location, unit="fahrenheit"):
+    """Get the current weather in a given location"""
+    if "tokyo" in location.lower():
+        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
+    elif "san francisco" in location.lower():
+        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
+    elif "paris" in location.lower():
+        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
+    else:
+        return json.dumps({"location": location, "temperature": "unknown"})
+
+messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_current_weather",
+            "description": "Get the current weather in a given location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA",
+                    },
+                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                },
+                "required": ["location"],
+            },
+        },
+    }
+]
+
+response = litellm.completion(
+    model="gpt-3.5-turbo-1106",
+    messages=messages,
+    tools=tools,
+    tool_choice="auto",  # auto is default, but we'll be explicit
+)
+print("\nLLM Response1:\n", response)
+response_message = response.choices[0].message
+tool_calls = response.choices[0].message.tool_calls
+```
+
 
 ### Setting Organization-ID for completion calls
 This can be set in one of the following ways: