diff --git a/src/lm_buddy/constants.py b/src/lm_buddy/constants.py
index 6239ecb..2ae0d5e 100644
--- a/src/lm_buddy/constants.py
+++ b/src/lm_buddy/constants.py
@@ -5,3 +5,8 @@
     "LM_BUDDY_HOME",
     str(Path.home() / ".lm_buddy"),
 )
+
+LM_BUDDY_RESULTS_PATH: str = os.getenv(
+    "LM_BUDDY_RESULTS",
+    f"{LM_BUDDY_HOME_PATH}/results",
+)
diff --git a/src/lm_buddy/jobs/evaluation/prometheus.py b/src/lm_buddy/jobs/evaluation/prometheus.py
index 7a0fb56..f916b76 100644
--- a/src/lm_buddy/jobs/evaluation/prometheus.py
+++ b/src/lm_buddy/jobs/evaluation/prometheus.py
@@ -17,7 +17,7 @@
 
 from lm_buddy.configs.huggingface import AutoTokenizerConfig
 from lm_buddy.configs.jobs.prometheus import PrometheusJobConfig
-from lm_buddy.constants import LM_BUDDY_HOME_PATH
+from lm_buddy.constants import LM_BUDDY_RESULTS_PATH
 from lm_buddy.jobs.asset_loader import HuggingFaceAssetLoader
 from lm_buddy.jobs.common import EvaluationResult
 from lm_buddy.preprocessing import format_dataset_with_prompt
@@ -161,8 +161,8 @@ def data_generator():
     result_dataset = Dataset.from_generator(data_generator)
 
     # Save dataset to disk
-    storage_path = config.evaluation.storage_path or LM_BUDDY_HOME_PATH
-    result_dataset_path = Path(storage_path) / "datasets" / config.name / "prometheus"
+    storage_path = config.evaluation.storage_path or LM_BUDDY_RESULTS_PATH
+    result_dataset_path = Path(storage_path) / config.name / "prometheus"
     result_dataset.save_to_disk(result_dataset_path)
 
     return result_dataset_path
diff --git a/src/lm_buddy/jobs/evaluation/ragas.py b/src/lm_buddy/jobs/evaluation/ragas.py
index d773460..9da7027 100644
--- a/src/lm_buddy/jobs/evaluation/ragas.py
+++ b/src/lm_buddy/jobs/evaluation/ragas.py
@@ -8,7 +8,7 @@
 from ragas.metrics import answer_relevancy, context_precision, context_recall, faithfulness
 
 from lm_buddy.configs.jobs.ragas import RagasJobConfig
-from lm_buddy.constants import LM_BUDDY_HOME_PATH
+from lm_buddy.constants import LM_BUDDY_RESULTS_PATH
 from lm_buddy.jobs.asset_loader import HuggingFaceAssetLoader
 from lm_buddy.jobs.common import EvaluationResult
 from lm_buddy.preprocessing import format_dataset_with_prompt
@@ -60,8 +60,8 @@ def run_eval(config: RagasJobConfig) -> Path:
     result_dataset = Dataset.from_pandas(result.to_pandas())
 
     # Save dataset to disk
-    storage_path = config.evaluation.storage_path or LM_BUDDY_HOME_PATH
-    result_dataset_path = Path(storage_path) / "datasets" / config.name / "ragas"
+    storage_path = config.evaluation.storage_path or LM_BUDDY_RESULTS_PATH
+    result_dataset_path = Path(storage_path) / config.name / "ragas"
     result_dataset.save_to_disk(result_dataset_path)
 
     return result_dataset_path