clarify workflow prompt

rdagent/components/coder/data_science/raw_data_loader/prompts.yaml

@@ -248,14 +248,14 @@ spec:
           - Integrate the following components into the workflow:
             - Data loading (`load_data.py`).
             - Feature engineering (`feature.py`).
-            - Model workflow for training and testing. 
-              The workflow should be flexible enough to handle either a single model or multiple models, with filenames (model_*.py) that are not determined at the outset.
-              For multiple model selection, utilize Python code to identify eligible models based on filenames, for example:
-              ```python
-              available_models = [f for f in os.listdir('.') if f.startswith('model_') and 'test' not in f]
-              ```
-            - Ensemble and decision-making (`ensemble.py`).
+            - Model workflow for training and testing (`model_*.py`). 
+            - Ensemble workflow that combines results from the model workflow to obtain the final prediction (`ensemble.py`).
           - Treat each component as a modular and callable Python function.
+          - The workflow script should be flexible enough to handle either a single model or multiple models, with filenames (model_*.py) that are not determined at the outset.
+            For multiple model selection, utilize Python code to identify eligible models based on filenames, for example:
+            ```python
+            available_models = [f for f in os.listdir('.') if f.startswith('model_') and 'test' not in f]
+            ```
         2. Feature Engineering
           - The feature engineering should be called only once. For example:
             `X_transformed, y_transformed, X_test_transformed = feat_eng(X, y, X_test)`