diff --git a/llm_core/llm_core/utils/llm_utils.py b/llm_core/llm_core/utils/llm_utils.py
index 6cacd4c0..4637b855 100644
--- a/llm_core/llm_core/utils/llm_utils.py
+++ b/llm_core/llm_core/utils/llm_utils.py
@@ -1,6 +1,7 @@
 from typing import Type, TypeVar, List
 from pydantic import BaseModel
 import tiktoken
+from langchain.chat_models import ChatOpenAI
 from langchain.base_language import BaseLanguageModel
 from langchain.prompts import (
     ChatPromptTemplate,
@@ -65,6 +66,18 @@ def check_prompt_length_and_omit_features_if_necessary(prompt: ChatPromptTemplat
     return prompt_input, False
 
 
+def supports_function_calling(model: BaseLanguageModel):
+    """Returns True if the model supports function calling, False otherwise
+
+    Args:
+        model (BaseLanguageModel): The model to check
+
+    Returns:
+        boolean: True if the model supports function calling, False otherwise
+    """
+    return isinstance(model, ChatOpenAI)
+
+
 def get_chat_prompt_with_formatting_instructions(
             model: BaseLanguageModel,
             system_message: str, 
@@ -84,9 +97,14 @@ def get_chat_prompt_with_formatting_instructions(
     Returns:
         ChatPromptTemplate: ChatPromptTemplate with formatting instructions (if necessary)
     """
+    if supports_function_calling(model):
+        system_message_prompt = SystemMessagePromptTemplate.from_template(system_message)
+        human_message_prompt = HumanMessagePromptTemplate.from_template(human_message)
+        return ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
+    
     output_parser = PydanticOutputParser(pydantic_object=pydantic_object)
-    system_message_prompt = SystemMessagePromptTemplate.from_template(system_message + "\n\n{format_instructions}")
+    system_message_prompt = SystemMessagePromptTemplate.from_template(system_message + "\n{format_instructions}")
     system_message_prompt.prompt.partial_variables = {"format_instructions": output_parser.get_format_instructions()}
     system_message_prompt.prompt.input_variables.remove("format_instructions")
-    human_message_prompt = HumanMessagePromptTemplate.from_template(human_message)
+    human_message_prompt = HumanMessagePromptTemplate.from_template(human_message + "\n\nJSON response following the provided schema:")
     return ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
\ No newline at end of file