diff --git a/src/bespokelabs/curator/llm/prompt_formatter.py b/src/bespokelabs/curator/llm/prompt_formatter.py
index 826398d2..4dae93ce 100644
--- a/src/bespokelabs/curator/llm/prompt_formatter.py
+++ b/src/bespokelabs/curator/llm/prompt_formatter.py
@@ -110,6 +110,9 @@ def response_to_response_format(self, response_message: str | dict) -> Optional[
         """
         # Response message is a string, which is converted to a dict
         # The dict is then used to construct the response_format Pydantic model
+        if self.response_format is None:
+            return response_message
+
         try:
             # First try to parse the response message as JSON
             if isinstance(response_message, str):
diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py
index 7e8d922f..51537125 100644
--- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py
+++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py
@@ -204,6 +204,11 @@ def run(
         parse_func_hash: str,
         prompt_formatter: PromptFormatter,
     ) -> Dataset:
+        # load from already completed dataset
+        output_dataset = self.attempt_loading_cached_dataset(working_dir, parse_func_hash)
+        if output_dataset is not None:
+            return output_dataset
+
         """Run completions using the online API with async processing."""
         logger.info(f"Running {self.__class__.__name__} completions with model: {self.model}")
 
diff --git a/src/bespokelabs/curator/request_processor/base_request_processor.py b/src/bespokelabs/curator/request_processor/base_request_processor.py
index 841223c0..6a5b2a30 100644
--- a/src/bespokelabs/curator/request_processor/base_request_processor.py
+++ b/src/bespokelabs/curator/request_processor/base_request_processor.py
@@ -315,19 +315,18 @@ def create_dataset_files(
                             failed_responses_count += 1
                             continue
 
-                        if prompt_formatter.response_format:
-                            try:
-                                response.response_message = (
-                                    self.prompt_formatter.response_to_response_format(
-                                        response.response_message
-                                    )
-                                )
-                            except (json.JSONDecodeError, ValidationError) as e:
-                                logger.warning(
-                                    "Skipping response due to error parsing response message into response format"
+                        try:
+                            response.response_message = (
+                                self.prompt_formatter.response_to_response_format(
+                                    response.response_message
                                 )
-                                failed_responses_count += 1
-                                continue
+                            )
+                        except (json.JSONDecodeError, ValidationError) as e:
+                            logger.warning(
+                                "Skipping response due to error parsing response message into response format"
+                            )
+                            failed_responses_count += 1
+                            continue
 
                         # parse_func can return a single row or a list of rows
                         if prompt_formatter.parse_func:
diff --git a/tests/cache/different_files/one.py b/tests/cache/different_files/one.py
index 10ff74d4..e5667add 100644
--- a/tests/cache/different_files/one.py
+++ b/tests/cache/different_files/one.py
@@ -1,32 +1,18 @@
 from bespokelabs.curator import LLM
 from datasets import Dataset
 import logging
-import argparse
 
 logger = logging.getLogger("bespokelabs.curator")
 logger.setLevel(logging.INFO)
 
 
-def main(delete_cache: bool = False):
-    dataset = Dataset.from_dict({"prompt": ["just say 'hi'"] * 3})
+dataset = Dataset.from_dict({"prompt": ["just say 'hi'"] * 3})
 
-    prompter = LLM(
-        prompt_func=lambda row: row["prompt"],
-        model_name="gpt-4o-mini",
-        response_format=None,
-        delete_cache=delete_cache,
-    )
+prompter = LLM(
+    prompt_func=lambda row: row["prompt"],
+    model_name="gpt-4o-mini",
+    response_format=None,
+)
 
-    dataset = prompter(dataset)
-    print(dataset.to_pandas())
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run prompter with cache control")
-    parser.add_argument(
-        "--delete-cache",
-        action="store_true",
-        help="Delete the cache before running",
-    )
-    args = parser.parse_args()
-    main(delete_cache=args.delete_cache)
+dataset = prompter(dataset)
+print(dataset.to_pandas())
diff --git a/tests/cache/different_files/two.py b/tests/cache/different_files/two.py
index 10ff74d4..e5667add 100644
--- a/tests/cache/different_files/two.py
+++ b/tests/cache/different_files/two.py
@@ -1,32 +1,18 @@
 from bespokelabs.curator import LLM
 from datasets import Dataset
 import logging
-import argparse
 
 logger = logging.getLogger("bespokelabs.curator")
 logger.setLevel(logging.INFO)
 
 
-def main(delete_cache: bool = False):
-    dataset = Dataset.from_dict({"prompt": ["just say 'hi'"] * 3})
+dataset = Dataset.from_dict({"prompt": ["just say 'hi'"] * 3})
 
-    prompter = LLM(
-        prompt_func=lambda row: row["prompt"],
-        model_name="gpt-4o-mini",
-        response_format=None,
-        delete_cache=delete_cache,
-    )
+prompter = LLM(
+    prompt_func=lambda row: row["prompt"],
+    model_name="gpt-4o-mini",
+    response_format=None,
+)
 
-    dataset = prompter(dataset)
-    print(dataset.to_pandas())
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run prompter with cache control")
-    parser.add_argument(
-        "--delete-cache",
-        action="store_true",
-        help="Delete the cache before running",
-    )
-    args = parser.parse_args()
-    main(delete_cache=args.delete_cache)
+dataset = prompter(dataset)
+print(dataset.to_pandas())
diff --git a/tests/cache/test_different_files.py b/tests/cache/test_different_files.py
index 6b18de07..31fe866b 100644
--- a/tests/cache/test_different_files.py
+++ b/tests/cache/test_different_files.py
@@ -16,17 +16,14 @@ def test_cache_behavior():
 
     # Run one.py twice and check for cache behavior
     print("RUNNING ONE.PY")
-    output1, _ = run_script(["python", "tests/cache_tests/different_files/one.py"])
-    print(output1)
+    output1, _ = run_script(["python", "tests/cache/different_files/one.py"])
     assert cache_hit_log not in output1, "First run of one.py should not hit cache"
 
     print("RUNNING ONE.PY AGAIN")
-    output2, _ = run_script(["python", "tests/cache_tests/different_files/one.py"])
-    print(output2)
+    output2, _ = run_script(["python", "tests/cache/different_files/one.py"])
     assert cache_hit_log in output2, "Second run of one.py should hit cache"
 
     # Run two.py and check for cache behavior
     print("RUNNING TWO.PY")
-    output3, _ = run_script(["python", "tests/cache_tests/different_files/two.py"])
-    print(output3)
+    output3, _ = run_script(["python", "tests/cache/different_files/two.py"])
     assert cache_hit_log in output3, "First run of two.py should hit cache"