diff --git a/src/chronos/base.py b/src/chronos/base.py
index 0f4b516..c2263b0 100644
--- a/src/chronos/base.py
+++ b/src/chronos/base.py
@@ -165,9 +165,11 @@ class BaseChronosPipeline(metaclass=PipelineRegistry):
         quantile_levels
             Quantile levels to compute
         validate_inputs
-            [ADVANCED] When False, skips validation. You must ensure: (1) df and future_df (if provided)
-            are sorted by (id_column, timestamp_column); (2) future_df (if provided) contains exactly
-            prediction_length rows per item. Defaults to True.
+            [ADVANCED] When True (default), validates dataframes before prediction. Setting
+            to False is faster but data errors may silently lead to wrong predictions. When
+            False, you must ensure: (1) all dataframes are sorted by (id_column, timestamp_column);
+            (2) future_df (if provided) has the same item IDs as df with exactly
+            prediction_length rows of future timestamps per item.
         freq
             Frequency string for timestamp generation (e.g., "H", "D", "W"). Can only be used when
             validate_inputs=False. When provided, skips frequency inference from the data.
diff --git a/src/chronos/chronos2/pipeline.py b/src/chronos/chronos2/pipeline.py
index f16570a..b0ebcae 100644
--- a/src/chronos/chronos2/pipeline.py
+++ b/src/chronos/chronos2/pipeline.py
@@ -865,9 +865,11 @@ class Chronos2Pipeline(BaseChronosPipeline):
             For optimal results, consider using a batch size around 100 (as used in the Chronos-2 technical report).
             - Cross-learning is most helpful when individual time series have limited historical context, as the model can leverage patterns from related series in the batch.
         validate_inputs
-            [ADVANCED] When False, skips validation. You must ensure: (1) df and future_df (if provided)
-            are sorted by (id_column, timestamp_column); (2) future_df (if provided) contains exactly
-            prediction_length rows per item. Defaults to True.
+            [ADVANCED] When True (default), validates dataframes before prediction. Setting
+            to False is faster but data errors may silently lead to wrong predictions. When
+            False, you must ensure: (1) all dataframes are sorted by (id_column, timestamp_column);
+            (2) future_df (if provided) has the same item IDs as df with exactly
+            prediction_length rows of future timestamps per item.
         freq
             Frequency string for timestamp generation (e.g., "h", "D", "W"). Can only be used when
             validate_inputs=False. When provided, skips frequency inference from the data.
diff --git a/src/chronos/df_utils.py b/src/chronos/df_utils.py
index bc09f37..e1f0b50 100644
--- a/src/chronos/df_utils.py
+++ b/src/chronos/df_utils.py
@@ -234,9 +234,11 @@ def convert_df_input_to_list_of_dicts_input(
         Frequency string for timestamp generation (e.g., "H", "D", "W"). Can only be used
         when validate_inputs=False. When provided, skips frequency inference from the data.
     validate_inputs
-        [ADVANCED] When False, skips validation. You must ensure: (1) df and future_df (if provided)
-        are sorted by (id_column, timestamp_column); (2) future_df (if provided) contains exactly
-        prediction_length rows per item. Defaults to True.
+        [ADVANCED] When True (default), validates dataframes before prediction. Setting
+        to False is faster but data errors may silently lead to wrong predictions. When
+        False, you must ensure: (1) all dataframes are sorted by (id_column, timestamp_column);
+        (2) future_df (if provided) has the same item IDs as df with exactly
+        prediction_length rows of future timestamps per item.
 
     Returns
     -------