diff --git a/docs/prompting/prompt_function.md b/docs/prompting/prompt_function.md
index a8d0617bc..43771747d 100644
--- a/docs/prompting/prompt_function.md
+++ b/docs/prompting/prompt_function.md
@@ -21,12 +21,12 @@ This is the easiest way to use Azure / OpenAI's function calling API.
     def list_fruits(n: int, color: str = 'red') -> list[str]:
         '''Generates a list of {{n}} {{color}} fruits'''
 
-    list_fruits(3, color = 'blue').serialize()
+    list_fruits(3, color = 'blue')
 
     ```
     This function can now be run and serialized to an Azure / OpenAI Function Calling payload.
 
-    ??? success "Click to see results: ```list_fruits(3, color = 'blue').serialize()```"
+    ??? success "Click to see results: ```list_fruits(3, color = 'blue')```"
         ```python
             {
             "messages": [
@@ -84,12 +84,12 @@ This is the easiest way to use Azure / OpenAI's function calling API.
     def list_fruits(n: int, color: str = 'red') -> list[Fruit]:
         '''Generates a list of {{n}} {{color}} fruits'''
 
-    list_fruits(3, color = 'blue').serialize()
+    list_fruits(3, color = 'blue')
 
     ```
     This function can now be run and serialized to an Azure / OpenAI Function Calling payload.
 
-    ??? success "Click to see results: ```list_fruits(3, color = 'blue').serialize()```"
+    ??? success "Click to see results: ```list_fruits(3, color = 'blue')```"
         ```python
             {
             "messages": [
@@ -168,12 +168,12 @@ This is the easiest way to use Azure / OpenAI's function calling API.
     def list_fruits(n: int, color: str = 'red') -> list[Fruit]:
         '''Generates a list of {{n}} {{color}} fruits'''
         
-    list_fruits(3, color = 'blue').serialize()
+    list_fruits(3, color = 'blue')
 
     ```
     This function can now be run and serialized to an Azure / OpenAI Function Calling payload.
 
-    ??? success "Click to see results: ```list_fruits(3, color = 'blue').serialize()```"
+    ??? success "Click to see results: ```list_fruits(3, color = 'blue')```"
         ```python
             {
             "messages": [
@@ -246,12 +246,12 @@ This is the easiest way to use Azure / OpenAI's function calling API.
     def list_fruits(n: int, color: str = 'red') -> list[Fruit]:
         '''Generates a list of {{n}} {{color}} {{'{{ response_model.__name__.lower() }}'}}'''
         
-    list_fruits(3, color = 'blue').serialize()
+    list_fruits(3, color = 'blue')
 
     ```
     This function can now be run and serialized to an Azure / OpenAI Function Calling payload.
 
-    ??? success "Click to see results: ```list_fruits(3, color = 'blue').serialize()```"
+    ??? success "Click to see results: ```list_fruits(3, color = 'blue')```"
         ```python
             {
                 "messages": [
@@ -324,7 +324,7 @@ This is the easiest way to use Azure / OpenAI's function calling API.
 
     This function can now be run and serialized to an Azure / OpenAI Function Calling payload.
 
-    ??? success "Click to see results: ```list_fruits(3, color = 'blue').serialize()```"
+    ??? success "Click to see results: ```list_fruits(3, color = 'blue')```"
         ```python
             {
                 "messages": [
@@ -385,7 +385,7 @@ This is the easiest way to use Azure / OpenAI's function calling API.
 
     This function can now be run and serialized to an Azure / OpenAI Function Calling payload.
 
-    ??? success "Click to see results: ```list_fruits(3, color = 'blue').serialize()```"
+    ??? success "Click to see results: ```list_fruits(3, color = 'blue')```"
         ```python
             {
                 "messages": [
@@ -456,7 +456,7 @@ This is the easiest way to use Azure / OpenAI's function calling API.
 
     This function can now be run and serialized to an Azure / OpenAI Function Calling payload.
 
-    ??? success "Click to see results: ```classify_fruits('tomato').serialize()```"
+    ??? success "Click to see results: ```classify_fruits('tomato')```"
         ```python
         {
         "messages": [
@@ -525,7 +525,7 @@ This is the easiest way to use Azure / OpenAI's function calling API.
 
     This function can now be run and serialized to an Azure / OpenAI Function Calling payload.
 
-    ??? success "```extract_fruits('There are red apples and yellow bananas.').serialize()```"
+    ??? success "```extract_fruits('There are red apples and yellow bananas.')```"
         ```python
         {
             "messages": [
diff --git a/src/marvin/components/ai_classifier.py b/src/marvin/components/ai_classifier.py
index 79d10112c..98e0a69d2 100644
--- a/src/marvin/components/ai_classifier.py
+++ b/src/marvin/components/ai_classifier.py
@@ -30,6 +30,7 @@ def ai_classifier_prompt(
         response_model_name="Index",
         response_model_description="The index of the most likely class.",
         response_model_field_name="index",
+        serialize_on_call=False,
         **kwargs,
     )
 
@@ -151,7 +152,7 @@ def get_prompt(
         ctx = ctx or cls.__metadata__.ctx or {}
         instructions = instructions or cls.__metadata__.instructions
         ctx["instructions"] = instructions or ctx.get("instructions", None)
-        return ai_classifier_prompt(cls, ctx=ctx, **kwargs)  # type: ignore
+        return ai_classifier_prompt(cls, ctx=ctx, **kwargs)  # type: ignore # noqa
 
     @classmethod
     def as_prompt(
diff --git a/src/marvin/components/ai_function.py b/src/marvin/components/ai_function.py
index 65e602c2c..81ec4cc7c 100644
--- a/src/marvin/components/ai_function.py
+++ b/src/marvin/components/ai_function.py
@@ -28,6 +28,7 @@ def ai_fn_prompt(
     @prompt_fn(
         ctx={"ctx": ctx or {}, "func": func, "inspect": inspect},
         response_model=return_annotation,
+        serialize_on_call=False,
         **kwargs,
     )
     def prompt_wrapper(*args: P.args, **kwargs: P.kwargs) -> None:  # type: ignore # noqa
diff --git a/src/marvin/components/ai_model.py b/src/marvin/components/ai_model.py
index d95386ee4..5ad28599e 100644
--- a/src/marvin/components/ai_model.py
+++ b/src/marvin/components/ai_model.py
@@ -33,6 +33,7 @@ def ai_model_prompt(
         response_model=cls,
         response_model_name="FormatResponse",
         response_model_description=description,
+        serialize_on_call=False,
     )
     def prompt_wrapper(text: str) -> None:  # type: ignore # noqa
         """
diff --git a/src/marvin/prompts/base.py b/src/marvin/prompts/base.py
index 7165a2848..183661c8c 100644
--- a/src/marvin/prompts/base.py
+++ b/src/marvin/prompts/base.py
@@ -263,6 +263,7 @@ def as_decorator(
         response_model_name: Optional[str] = None,
         response_model_description: Optional[str] = None,
         response_model_field_name: Optional[str] = None,
+        serialize_on_call: bool = True,
     ) -> Union[
         Callable[[Callable[P, None]], Callable[P, None]],
         Callable[[Callable[P, None]], Callable[P, Self]],
@@ -289,6 +290,8 @@ def wrapper(func: Callable[P, Any], *args: P.args, **kwargs: P.kwargs) -> Self:
                 response_model_field_name=response_model_field_name,
             )
             response.__doc__ = func.__doc__
+            if serialize_on_call:
+                return response.serialize()
             return response
 
         if func is not None: