diff --git a/plugin-script-python/src/main/java/io/kestra/plugin/scripts/python/Script.java b/plugin-script-python/src/main/java/io/kestra/plugin/scripts/python/Script.java
index 719533e..3163e31 100644
--- a/plugin-script-python/src/main/java/io/kestra/plugin/scripts/python/Script.java
+++ b/plugin-script-python/src/main/java/io/kestra/plugin/scripts/python/Script.java
@@ -111,16 +111,18 @@ with open('{{ outputs.previousTaskId.uri }}', 'r') as f:
                 tasks:
                   - id: python
                     type: io.kestra.plugin.scripts.python.Script
+                    outputFiles:
+                      - "myfile.txt"
                     script: |
-                       f = open("{{ outputDir }}/myfile.txt", "a")
-                       f.write("Hello from a Kestra task!")
-                       f.close()   
+                      f = open("myfile.txt", "a")
+                      f.write("Hello from a Kestra task!")
+                      f.close()
                 """
         ),
         @Example(
             full = true,
             title = """
-            If you want to generate files in your script to make them available for download and use in downstream tasks, you can leverage the `{{outputDir}}` expression. Files stored in that directory will be persisted in Kestra's internal storage. The first task in this example creates a file `'myfile.txt'` and the next task can access it by leveraging the syntax `{{outputs.yourTaskId.outputFiles['yourFileName.fileExtension']}}`.
+            If you want to generate files in your script to make them available for download and use in downstream tasks, you can leverage the `outputFiles` property as shown in the example above. Files will be persisted in Kestra's internal storage. The first task in this example creates a file `'clean_dataset.csv'` and the next task can access it by leveraging the syntax `{{outputs.yourTaskId.outputFiles['yourFileName.fileExtension']}}`.
             """,
             code = """     
                 id: python_outputs
@@ -130,19 +132,21 @@ with open('{{ outputs.previousTaskId.uri }}', 'r') as f:
                   - id: clean_dataset
                     type: io.kestra.plugin.scripts.python.Script
                     containerImage: ghcr.io/kestra-io/pydata:latest
+                    outputFiles:
+                      - "clean_dataset.csv"
                     script: |
                       import pandas as pd
                       df = pd.read_csv("https://huggingface.co/datasets/kestra/datasets/raw/main/csv/messy_dataset.csv")
-                      
+
                       # Replace non-numeric age values with NaN
                       df["Age"] = pd.to_numeric(df["Age"], errors="coerce")
-                
+
                       # mean imputation: fill NaN values with the mean age
                       mean_age = int(df["Age"].mean())
                       print(f"Filling NULL values with mean: {mean_age}")
                       df["Age"] = df["Age"].fillna(mean_age)
-                      df.to_csv("{{ outputDir }}/clean_dataset.csv", index=False)
-                
+                      df.to_csv("clean_dataset.csv", index=False)
+
                   - id: readFileFromPython
                     type: io.kestra.plugin.scripts.shell.Commands
                     taskRunner: