diff --git a/model_server/app/function_calling/hallucination_handler.py b/model_server/app/function_calling/hallucination_handler.py
index acbda584..dc8e8ee0 100644
--- a/model_server/app/function_calling/hallucination_handler.py
+++ b/model_server/app/function_calling/hallucination_handler.py
@@ -212,7 +212,7 @@ class HallucinationStateHandler:
                 if (
                     len(self.mask) > 1
                     and self.mask[-2] != "v"
-                    and not check_parameter_property(
+                    and not is_parameter_property(
                         self.function_properties[self.function_name],
                         self.parameter_name[-1],
                         "default",
diff --git a/model_server/app/tests/test_hallucination.py b/model_server/app/tests/test_hallucination.py
index 25ad3303..8c23324b 100644
--- a/model_server/app/tests/test_hallucination.py
+++ b/model_server/app/tests/test_hallucination.py
@@ -47,14 +47,102 @@ if type(function_description) != list:
 
 @pytest.mark.parametrize("case", test_cases)
 def test_hallucination(case):
-    state = HallucinationStateHandler()
-    state.process_function(function_description)
+    state = HallucinationStateHandler(
+        response_iterator=None, function=function_description
+    )
     for token, logprob in zip(case["tokens"], case["logprobs"]):
         if token != "</tool_call>":
-            state.current_token = token
-            state.tokens.append(token)
-            state.logprobs.append(logprob)
-            state.process_token()
+            state.check_token_hallucination(token, logprob)
             if state.hallucination:
                 break
     assert state.hallucination == case["expect"]
+
+
+@pytest.mark.parametrize("is_hallucinate_sample", [True, False])
+def test_hallucination_prompt(is_hallucinate_sample):
+    TASK_PROMPT = """
+    You are a helpful assistant.
+    """.strip()
+
+    TOOL_PROMPT = """
+    # Tools
+
+    You may call one or more functions to assist with the user query.
+
+    You are provided with function signatures within <tools></tools> XML tags:
+    <tools>
+    {tool_text}
+    </tools>
+    """.strip()
+
+    FORMAT_PROMPT = """
+    For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+    <tool_call>
+    {"name": <function-name>, "arguments": <args-json-object>}
+    </tool_call>
+    """.strip()
+
+    def convert_tools(tools):
+        return "\n".join([json.dumps(tool) for tool in tools])
+
+    def format_prompt(tools):
+        tool_text = convert_tools(tools)
+
+        return (
+            TASK_PROMPT
+            + "\n\n"
+            + TOOL_PROMPT.format(tool_text=tool_text)
+            + "\n\n"
+            + FORMAT_PROMPT
+            + "\n"
+        )
+
+    openai_format_tools = [get_weather_api]
+
+    system_prompt = format_prompt(openai_format_tools)
+
+    from openai import OpenAI
+
+    client = OpenAI(base_url="https://api.fc.archgw.com/v1", api_key="EMPTY")
+
+    # List models API
+    model = client.models.list().data[0].id
+    assert model == "Arch-Function"
+    if not is_hallucinate_sample:
+        messages = [
+            {"role": "system", "content": system_prompt},
+            # {"role": "user", "content": "can you help me check weather?"},
+            {"role": "user", "content": "How is the weather in Seattle in 7 days?"},
+            # {"role": "assistant", "content": "Of course!"},
+            # {"role": "user", "content": "Seattle please"}
+        ]
+    else:
+        messages = [
+            {"role": "system", "content": system_prompt},
+            # {"role": "user", "content": "can you help me check weather?"},
+            {"role": "user", "content": "How is the weather in Seattle in?"},
+            # {"role": "assistant", "content": "Of course!"},
+            # {"role": "user", "content": "Seattle please"}
+        ]
+
+    extra_body = {
+        "temperature": 0.6,
+        "top_p": 1.0,
+        "top_k": 50,
+        # "continue_final_message": True,
+        # "add_generation_prompt": False,
+        "logprobs": True,
+        "top_logprobs": 10,
+    }
+
+    resp = client.chat.completions.create(
+        model="Arch-Function", messages=messages, extra_body=extra_body, stream=True
+    )
+
+    hallu = HallucinationStateHandler(
+        response_iterator=resp, function=function_description
+    )
+
+    for token in hallu:
+        assert len(hallu.tokens) >= 0
+    assert hallu.hallucination == is_hallucinate_sample