diff --git a/bigframes/ml/llm.py b/bigframes/ml/llm.py
index 93e2ba825f..78f3369daf 100644
--- a/bigframes/ml/llm.py
+++ b/bigframes/ml/llm.py
@@ -17,6 +17,7 @@
 from __future__ import annotations
 
 from typing import cast, Literal, Optional, Union
+import warnings
 
 import bigframes
 from bigframes import clients, constants
@@ -24,15 +25,22 @@
 from bigframes.ml import base, core, globals, utils
 import bigframes.pandas as bpd
 
-_REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT = "text-bison"
-_REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT = "text-bison-32k"
-_TEXT_GENERATE_RESULT_COLUMN = "ml_generate_text_llm_result"
+_TEXT_GENERATOR_BISON_ENDPOINT = "text-bison"
+_TEXT_GENERATOR_BISON_32K_ENDPOINT = "text-bison-32k"
+_TEXT_GENERATOR_ENDPOINTS = (
+    _TEXT_GENERATOR_BISON_ENDPOINT,
+    _TEXT_GENERATOR_BISON_32K_ENDPOINT,
+)
 
-_REMOTE_EMBEDDING_GENERATOR_MODEL_ENDPOINT = "textembedding-gecko"
-_REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT = (
-    "textembedding-gecko-multilingual"
+_EMBEDDING_GENERATOR_GECKO_ENDPOINT = "textembedding-gecko"
+_EMBEDDING_GENERATOR_GECKO_MULTILINGUAL_ENDPOINT = "textembedding-gecko-multilingual"
+_EMBEDDING_GENERATOR_ENDPOINTS = (
+    _EMBEDDING_GENERATOR_GECKO_ENDPOINT,
+    _EMBEDDING_GENERATOR_GECKO_MULTILINGUAL_ENDPOINT,
 )
-_EMBED_TEXT_RESULT_COLUMN = "text_embedding"
+
+_ML_GENERATE_TEXT_STATUS = "ml_generate_text_status"
+_ML_EMBED_TEXT_STATUS = "ml_embed_text_status"
 
 
 class PaLM2TextGenerator(base.Predictor):
@@ -90,18 +98,16 @@ def _create_bqml_model(self):
             connection_id=connection_name_parts[2],
             iam_role="aiplatform.user",
         )
-        if self.model_name == _REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT:
-            options = {
-                "endpoint": _REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT,
-            }
-        elif self.model_name == _REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT:
-            options = {
-                "endpoint": _REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT,
-            }
-        else:
+
+        if self.model_name not in _TEXT_GENERATOR_ENDPOINTS:
             raise ValueError(
-                f"Model name {self.model_name} is not supported. We only support {_REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT} and {_REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT}."
+                f"Model name {self.model_name} is not supported. We only support {', '.join(_TEXT_GENERATOR_ENDPOINTS)}."
             )
+
+        options = {
+            "endpoint": self.model_name,
+        }
+
         return self._bqml_model_factory.create_remote_model(
             session=self.session, connection_name=self.connection_name, options=options
         )
@@ -182,7 +188,16 @@ def predict(
             "top_p": top_p,
             "flatten_json_output": True,
         }
-        return self._bqml_model.generate_text(X, options)
+
+        df = self._bqml_model.generate_text(X, options)
+
+        if (df[_ML_GENERATE_TEXT_STATUS] != "").any():
+            warnings.warn(
+                f"Some predictions failed. Check column {_ML_GENERATE_TEXT_STATUS} for detailed status. You may want to filter the failed rows and retry.",
+                RuntimeWarning,
+            )
+
+        return df
 
 
 class PaLM2TextEmbeddingGenerator(base.Predictor):
@@ -241,19 +256,15 @@ def _create_bqml_model(self):
             connection_id=connection_name_parts[2],
             iam_role="aiplatform.user",
         )
-        if self.model_name == "textembedding-gecko":
-            options = {
-                "endpoint": _REMOTE_EMBEDDING_GENERATOR_MODEL_ENDPOINT,
-            }
-        elif self.model_name == _REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT:
-            options = {
-                "endpoint": _REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT,
-            }
-        else:
+
+        if self.model_name not in _EMBEDDING_GENERATOR_ENDPOINTS:
             raise ValueError(
-                f"Model name {self.model_name} is not supported. We only support {_REMOTE_EMBEDDING_GENERATOR_MODEL_ENDPOINT} and {_REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT}."
+                f"Model name {self.model_name} is not supported. We only support {', '.join(_EMBEDDING_GENERATOR_ENDPOINTS)}."
             )
 
+        options = {
+            "endpoint": self.model_name,
+        }
         return self._bqml_model_factory.create_remote_model(
             session=self.session, connection_name=self.connection_name, options=options
         )
@@ -284,4 +295,13 @@ def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame:
         options = {
             "flatten_json_output": True,
         }
-        return self._bqml_model.generate_text_embedding(X, options)
+
+        df = self._bqml_model.generate_text_embedding(X, options)
+
+        if (df[_ML_EMBED_TEXT_STATUS] != "").any():
+            warnings.warn(
+                f"Some predictions failed. Check column {_ML_EMBED_TEXT_STATUS} for detailed status. You may want to filter the failed rows and retry.",
+                RuntimeWarning,
+            )
+
+        return df