diff --git a/api/core/workflow/nodes/question_classifier/question_classifier_node.py b/api/core/workflow/nodes/question_classifier/question_classifier_node.py
index ceebfe2e25..832964fa59 100644
--- a/api/core/workflow/nodes/question_classifier/question_classifier_node.py
+++ b/api/core/workflow/nodes/question_classifier/question_classifier_node.py
@@ -188,11 +188,10 @@ class QuestionClassifierNode(LLMNode):
             prompt_messages.append(user_prompt_message_3)
             return prompt_messages
         elif model_mode == ModelMode.COMPLETION:
-            prompt_messages.append(CompletionModelPromptTemplate(
+            return CompletionModelPromptTemplate(
                 text=QUESTION_CLASSIFIER_COMPLETION_PROMPT.format(input_text=input_text, categories=class_names_str,
                                                                   classification_instructions=instruction)
-            ))
+            )
 
-            return prompt_messages
         else:
             raise ValueError(f"Model mode {model_mode} not support.")
diff --git a/api/core/workflow/nodes/question_classifier/template_prompts.py b/api/core/workflow/nodes/question_classifier/template_prompts.py
index 672d373741..faf44269ac 100644
--- a/api/core/workflow/nodes/question_classifier/template_prompts.py
+++ b/api/core/workflow/nodes/question_classifier/template_prompts.py
@@ -48,15 +48,17 @@ Your task is to assign one categories ONLY to the input text and only one catego
 The input text is in the variable text_field. Categories are specified as a comma-separated list in the variable categories or left empty for automatic determination. Classification instructions may be included to improve the classification accuracy. 
 ### Constraint 
 DO NOT include anything other than the JSON array in your response.
-### Example 
-Input: 
-{{"input_text": ["I recently had a great experience with your company. The service was prompt and the staff was very friendly."],"categories": ["Customer Service, Satisfaction, Sales, Product"], "classification_instructions": ["classify the text based on the feedback provided by customer"]}}
-{{"input_text": ["bad service, slow to bring the food"],"categories": ["Food Quality, Experience, Price" ], "classification_instructions": []}}
-Output: 
-{{"keywords": ["recently", "great experience", "company", "service", "prompt", "staff", "friendly"],"categories": ["Customer Service"]}}
-{{"keywords": ["bad service", "slow", "food", "tip", "terrible", "waitresses"],"categories": ["Experience""]}}
+### Example
+Here is the chat example between human and assistant, inside <example></example> XML tags.
+<example>
+User:{{"input_text": ["I recently had a great experience with your company. The service was prompt and the staff was very friendly."],"categories": ["Customer Service, Satisfaction, Sales, Product"], "classification_instructions": ["classify the text based on the feedback provided by customer"]}}
+Assistant:{{"keywords": ["recently", "great experience", "company", "service", "prompt", "staff", "friendly"],"categories": ["Customer Service"]}}
+User:{{"input_text": ["bad service, slow to bring the food"],"categories": ["Food Quality, Experience, Price" ], "classification_instructions": []}}
+Assistant:{{"keywords": ["recently", "great experience", "company", "service", "prompt", "staff", "friendly"],"categories": ["Customer Service"]}}{{"keywords": ["bad service", "slow", "food", "tip", "terrible", "waitresses"],"categories": ["Experience""]}}
+</example> 
 ### Memory
 Here is the chat histories between human and assistant, inside <histories></histories> XML tags.
 ### User Input
-{{"input_text" : ["{input_text}"], "class" : ["{class}"],"classification_instruction" : ["{classification_instructions}"]}}
+{{"input_text" : ["{input_text}"], "categories" : ["{categories}"],"classification_instruction" : ["{classification_instructions}"]}}
+### Assistant Output
 """
\ No newline at end of file