diff --git a/keras/src/losses/losses.py b/keras/src/losses/losses.py
index 8b29a4af2a0e..cea6d197c066 100644
--- a/keras/src/losses/losses.py
+++ b/keras/src/losses/losses.py
@@ -2143,10 +2143,21 @@ def categorical_focal_crossentropy(
 
     >>> y_true = [[0, 1, 0], [0, 0, 1]]
     >>> y_pred = [[0.05, 0.9, 0.05], [0.1, 0.85, 0.05]]
-    >>> loss = keras.losses.categorical_focal_crossentropy(y_true, y_pred)
-    >>> assert loss.shape == (2,)
-    >>> loss
+    >>> # In this instance, the second example is the 'harder' example.
+    >>> focal_loss = keras.losses.categorical_focal_crossentropy(y_true, y_pred)
+    >>> assert focal_loss.shape == (2,)
+    >>> focal_loss
     array([2.63401289e-04, 6.75912094e-01], dtype=float32)
+    >>> # Compare with categorical_crossentropy
+    >>> cce_loss = keras.losses.categorical_crossentropy(
+    ...        y_true, y_pred)
+    >>> cce_loss
+    array([0.10536054, 2.9957323], dtype=float32)
+    >>> # Categorical focal crossentropy loss attributes more importance to the
+    >>> # harder example which results in a higher loss for the second example
+    >>> # when normalized by categorical cross entropy loss
+    >>> focal_loss/cce_loss
+    array([0.0025  , 0.225625], dtype=float32)
     """
     if isinstance(axis, bool):
         raise ValueError(
@@ -2367,11 +2378,11 @@ def binary_focal_crossentropy(
     >>> # 'easier' example.
     >>> focal_loss = keras.losses.binary_focal_crossentropy(
     ...        y_true, y_pred, gamma=2)
-    >>> assert loss.shape == (2,)
+    >>> assert focal_loss.shape == (2,)
     >>> focal_loss
     array([0.330, 0.206], dtype=float32)
     >>> # Compare with binary_crossentropy
-    >>> bce_loss = keras.losses.binary_focal_crossentropy(
+    >>> bce_loss = keras.losses.binary_crossentropy(
     ...        y_true, y_pred)
     >>> bce_loss
     array([0.916, 0.714], dtype=float32)
@@ -2379,7 +2390,7 @@ def binary_focal_crossentropy(
     >>> # harder example which results in a higher loss for the first batch
     >>> # when normalized by binary cross entropy loss
     >>> focal_loss/bce_loss
-    array([0.360, 0.289]
+    array([0.360, 0.289], dtype=float32)
     """
     y_pred = ops.convert_to_tensor(y_pred)
     y_true = ops.cast(y_true, y_pred.dtype)