diff --git a/docs/concepts/parallel.md b/docs/concepts/parallel.md
index 506010234..8bb5b52fb 100644
--- a/docs/concepts/parallel.md
+++ b/docs/concepts/parallel.md
@@ -5,19 +5,19 @@ description: Learn about OpenAI's experimental parallel function calling to redu
 
 # Parallel Tools
 
-Parallel Tool Calling is a feature that allows you to call multiple functions in a single request. This makes it faster to get a response from the language model, especially if your tool calls are independent of each other.
+Parallel Function Calling is a feature that allows you to call multiple functions in a single request.
 
 !!! warning "Experimental Feature"
 
-    Parallel tool calling is only supported by Gemini and OpenAI at the moment
+    Parallel Function calling is only supported by Google and OpenAI at the moment. Make sure to use the equivalent parallel tool `mode` for your client.
 
 ## Understanding Parallel Function Calling
 
-By using parallel function callings that allow you to call multiple functions in a single request, you can significantly reduce the latency of your application without having to use tricks with now one builds a schema.
+Parallel Function Calling helps you to significantly reduce the latency of your application without having to build a parent schema as a wrapper around these tool calls.
 
 === "OpenAI"
 
-    ```python hl_lines="19 31"
+    ```python hl_lines="20 32"
     from __future__ import annotations
 
     import openai
@@ -38,7 +38,7 @@ By using parallel function callings that allow you to call multiple functions in
 
     client = instructor.from_openai(
         openai.OpenAI(), mode=instructor.Mode.PARALLEL_TOOLS
-    )  # (1)!
+    )
 
     function_calls = client.chat.completions.create(
         model="gpt-4o-mini",
@@ -49,7 +49,7 @@ By using parallel function callings that allow you to call multiple functions in
                 "content": "What is the weather in toronto and dallas and who won the super bowl?",
             },
         ],
-        response_model=Iterable[Weather | GoogleSearch],  # (2)!
+        response_model=Iterable[Weather | GoogleSearch],
     )
 
     for fc in function_calls:
@@ -61,7 +61,7 @@ By using parallel function callings that allow you to call multiple functions in
 
 === "Vertex AI"
 
-    ```python
+    ```python hl_lines="20 30"
     import instructor
     import vertexai
     from vertexai.generative_models import GenerativeModel
@@ -82,7 +82,7 @@ By using parallel function callings that allow you to call multiple functions in
     client = instructor.from_vertexai(
         GenerativeModel("gemini-1.5-pro-preview-0409"),
         mode=instructor.Mode.VERTEXAI_PARALLEL_TOOLS
-    )  # (1)!
+    )
 
     function_calls = client.create(
         messages=[
@@ -91,7 +91,7 @@ By using parallel function callings that allow you to call multiple functions in
                 "content": "What is the weather in toronto and dallas and who won the super bowl?",
             },
         ],
-        response_model=Iterable[Weather | GoogleSearch],  # (2)!
+        response_model=Iterable[Weather | GoogleSearch],
     )
 
     for fc in function_calls:
@@ -101,7 +101,6 @@ By using parallel function callings that allow you to call multiple functions in
         #> query='who won the super bowl'
     ```
 
-1. Set the mode to `PARALLEL_TOOLS` to enable parallel function calling.
-2. Set the response model to `Iterable[Weather | GoogleSearch]` to indicate that the response will be a list of `Weather` and `GoogleSearch` objects. This is necessary because the response will be a list of objects, and we need to specify the types of the objects in the list.
+We need to set the response model to `Iterable[Weather | GoogleSearch]` to indicate that the response will be a list of `Weather` and `GoogleSearch` objects.
 
-Noticed that the `response_model` Must be in the form `Iterable[Type1 | Type2 | ...]` or `Iterable[Type1]` where `Type1` and `Type2` are the types of the objects that will be returned in the response.
+This is necessary because the response will be a list of objects, and we need to specify the types of the objects in the list. This returns an iterable which you can then iterate over