diff --git a/bigframes/bigquery/_operations/ai.py b/bigframes/bigquery/_operations/ai.py
index e56292d64f..fd7dafe95f 100644
--- a/bigframes/bigquery/_operations/ai.py
+++ b/bigframes/bigquery/_operations/ai.py
@@ -58,14 +58,14 @@ def generate(
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> country = bpd.Series(["Japan", "Canada"])
-        >>> bbq.ai.generate(("What's the capital city of ", country, " one word only"))
-        0    {'result': 'Tokyo\\n', 'full_response': '{"cand...
-        1    {'result': 'Ottawa\\n', 'full_response': '{"can...
+        >>> bbq.ai.generate(("What's the capital city of ", country, " one word only")) # doctest: +SKIP
+        0    {'result': 'Tokyo', 'full_response': '{"cand...
+        1    {'result': 'Ottawa', 'full_response': '{"can...
         dtype: struct<result: string, full_response: extension<dbjson<JSONArrowType>>, status: string>[pyarrow]
 
-        >>> bbq.ai.generate(("What's the capital city of ", country, " one word only")).struct.field("result")
-        0     Tokyo\\n
-        1    Ottawa\\n
+        >>> bbq.ai.generate(("What's the capital city of ", country, " one word only")).struct.field("result") # doctest: +SKIP
+        0     Tokyo
+        1    Ottawa
         Name: result, dtype: string
 
     You get structured output when the `output_schema` parameter is set: