diff --git a/rdagent/components/coder/data_science/model/prompts.yaml b/rdagent/components/coder/data_science/model/prompts.yaml
index 7ed96275..faa0f6b8 100644
--- a/rdagent/components/coder/data_science/model/prompts.yaml
+++ b/rdagent/components/coder/data_science/model/prompts.yaml
@@ -36,6 +36,7 @@ model_coder:
     --------- Feature Engineering Code ---------
     {{ feature_code }}
     2. You should avoid using logging module to output information in your generated code, and instead use the print() function.
+    3. You can decide whether to use AutoML based on the characteristics of the task.
 
     ## Output Format
     {% if out_spec %}
diff --git a/rdagent/components/coder/data_science/raw_data_loader/prompts.yaml b/rdagent/components/coder/data_science/raw_data_loader/prompts.yaml
index a51e4e7b..97f3a1d4 100644
--- a/rdagent/components/coder/data_science/raw_data_loader/prompts.yaml
+++ b/rdagent/components/coder/data_science/raw_data_loader/prompts.yaml
@@ -23,15 +23,15 @@ spec:
     The specifications for each step should be tailored to the competition information provided. 
     
     Your specification should consists two parts:
-    1. The function definition in code format with detailed annotation to each parameter and return value.
-    2. A correct, concise, and complete docstring to the function that explains the purpose of the function, the input parameters, and the output.
-    3. Additional information or notes that the coder should consider while implementing the function.
-    Your specifications should not include any code implementation, only the function definition and docstring.
+    1. The function definition in code format, including type annotations and a clear, complete docstring that describes the function's purpose, input parameters, return value, and any relevant exceptions.
+    2. Additional information or notes that the coder should consider while implementing the function.
+    
+    Your specifications should include only the function definition and docstring, without any code implementation or inline comments.
 
     ----------- Competition Information -----------
     {{ competition_info }}
 
-    -----------Folder Description (All path are relative to the data folder) ---------
+    ----------- Folder Description (All path are relative to the data folder) ---------
     - Ensure that all columns in sample_submission can be generated.
     {{ folder_spec }}
 
diff --git a/rdagent/components/coder/data_science/workflow/prompts.yaml b/rdagent/components/coder/data_science/workflow/prompts.yaml
index ca6ab3ce..758dc6aa 100644
--- a/rdagent/components/coder/data_science/workflow/prompts.yaml
+++ b/rdagent/components/coder/data_science/workflow/prompts.yaml
@@ -38,8 +38,9 @@ workflow_coder:
       - Each functionality is in a separate Python file.
     2. Your task is only to integrate the existing processes of load_data, feature, model, and ensemble into a complete workflow. Do not edit or modify the existing Python files. The final step should output the predictions in the required format.
     3. The user may provide specific code organization rules and instructions. Ensure that the integration follows the given framework and structure.
-    4. After predicting the output, print the shape and other information of the output to stdout to help the evaluator assess the code.
-    5. You should avoid using logging module to output information in your generated code, and instead use the print() function.
+    4. Implement cross-validation when training models to evaluate performance on different subsets of the data if you think it is necessary.
+    5. After predicting the output, print the shape and other information of the output to stdout to help the evaluator assess the code.
+    6. You should avoid using logging module to output information in your generated code, and instead use the print() function.
 
     ## Output Format
     Please response the code in the following json format. Here is an example structure for the JSON output: