diff --git a/examples/studio/chat/chat_function_calling_multiple_tools.py b/examples/studio/chat/chat_function_calling_multiple_tools.py
index 34b3c130..b61deeb2 100644
--- a/examples/studio/chat/chat_function_calling_multiple_tools.py
+++ b/examples/studio/chat/chat_function_calling_multiple_tools.py
@@ -78,7 +78,7 @@ def get_sunset_hour(place: str, date: str) -> str:
 
 """ AI models can be error-prone, it's crucial to ensure that the tool calls align with the expectations.
 The below code snippet demonstrates how to handle tool calls in the response and invoke the tool function
-to get the delivery date for the user's order. After retrieving the delivery date, we pass the response back
+to get the weather forcast and expected sunset hour. After retrieving them, we pass the responses back
 to the AI model to continue the conversation, using the ToolMessage object. """
 
 assistant_message = response.choices[0].message
@@ -100,7 +100,7 @@ def get_sunset_hour(place: str, date: str) -> str:
                 print(f"Got unexpected arguments in function call - {args}")
 
         elif tool_call.function.name == "get_sunset_hour":
-            """Verify get_sunset_hour tool call arguments and invoke the function to get the weather forecast:"""
+            """Verify get_sunset_hour tool call arguments and invoke the function to get the sunset hour:"""
             func_arguments = tool_call.function.arguments
             args = json.loads(func_arguments)
 
@@ -116,7 +116,7 @@ def get_sunset_hour(place: str, date: str) -> str:
     print("No tool calls found")
 
 if too_call_id_to_result:
-    """Continue the conversation by passing the sunset and weather back to the AI model:"""
+    """Continue the conversation by passing the sunset and weather info back to the AI model:"""
 
     for tool_id_called, result in too_call_id_to_result.items():
         tool_message = ToolMessage(role="tool", tool_call_id=tool_id_called, content=str(result))