diff --git a/litellm/llms/sagemaker.py b/litellm/llms/sagemaker.py
index 96d06bc20..2bfa9f82a 100644
--- a/litellm/llms/sagemaker.py
+++ b/litellm/llms/sagemaker.py
@@ -158,6 +158,7 @@ def completion(
         )
     except Exception as e: 
         raise SagemakerError(status_code=500, message=f"{str(e)}")
+    
     response = response["Body"].read().decode("utf8")
     ## LOGGING
     logging_obj.post_call(
@@ -171,10 +172,17 @@ def completion(
     completion_response = json.loads(response)
     try:
         completion_response_choices = completion_response[0]
+        completion_output = ""
         if "generation" in completion_response_choices:
-            model_response["choices"][0]["message"]["content"] = completion_response_choices["generation"]
+            completion_output += completion_response_choices["generation"]
         elif "generated_text" in completion_response_choices:
-            model_response["choices"][0]["message"]["content"] = completion_response_choices["generated_text"]
+            completion_output += completion_response_choices["generated_text"]
+        
+        # check if the prompt template is part of output, if so - filter it out 
+        if completion_output.startswith(prompt) and "<s>" in prompt:
+            completion_output = completion_output.replace(prompt, "", 1)
+
+        model_response["choices"][0]["message"]["content"] = completion_output
     except:
         raise SagemakerError(message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}", status_code=500)
 
diff --git a/litellm/tests/test_custom_logger.py b/litellm/tests/test_custom_logger.py
index 8209520e4..c50f2b1ee 100644
--- a/litellm/tests/test_custom_logger.py
+++ b/litellm/tests/test_custom_logger.py
@@ -298,7 +298,7 @@ async def test_async_custom_handler_embedding_optional_param():
     customHandler_optional_params = MyCustomHandler()
     litellm.callbacks = [customHandler_optional_params]
     response = await litellm.aembedding(
-                model="text-embedding-ada-002", 
+                model="azure/azure-embedding-model", 
                 input = ["hello world"],
                 user = "John"
             )