diff --git a/docs/images/product_recommendation_overview.png b/docs/images/product_recommendation_overview.png
new file mode 100644
index 000000000..46e4c8710
Binary files /dev/null and b/docs/images/product_recommendation_overview.png differ
diff --git a/feathr_project/feathrcli/data/feathr_user_workspace/product_rating_demo.ipynb b/feathr_project/feathrcli/data/feathr_user_workspace/product_recommendation_demo.ipynb
similarity index 97%
rename from feathr_project/feathrcli/data/feathr_user_workspace/product_rating_demo.ipynb
rename to feathr_project/feathrcli/data/feathr_user_workspace/product_recommendation_demo.ipynb
index 05502119d..65774bf1c 100644
--- a/feathr_project/feathrcli/data/feathr_user_workspace/product_rating_demo.ipynb
+++ b/feathr_project/feathrcli/data/feathr_user_workspace/product_recommendation_demo.ipynb
@@ -19,7 +19,8 @@
     "6. Materialize feature value to online store.\n",
     "7. Fetch feature value in real-time from online store for online scoring.\n",
     "\n",
-    "In this tutorial, we use Feathr Feature Store to create a model that predict users rating for a product."
+    "In this tutorial, we use Feathr Feature Store to create a model that predict users rating for a product. The feature flow is as below:\n",
+    "[Feature Flow](https://github.com/linkedin/feathr/blob/main/docs/images/product_recommendation_overview.png?raw=true)"
    ]
   },
   {
@@ -328,7 +329,7 @@
     "# User purchase history dataset.\n",
     "# Used to generate user features. This is activity type data, so we need to use aggregation to genearte features.\n",
     "import pandas as pd\n",
-    "pd.read_csv(\"https://azurefeathrstorage.blob.core.windows.net/public/sample_data/product_recommendation_sample/user_purchase_history_mock_data\")"
+    "pd.read_csv(\"https://azurefeathrstorage.blob.core.windows.net/public/sample_data/product_recommendation_sample/user_purchase_history_mock_data.csv\")"
    ]
   },
   {
@@ -536,8 +537,7 @@
     "if client.spark_runtime == 'databricks':\n",
     "    output_path = 'dbfs:/feathrazure_test.avro'\n",
     "else:\n",
-    "    # output_path = feathr_output_path\n",
-    "    output_path = 'abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/feathr_output/product_recommendation_output/'\n",
+    "    output_path = feathr_output_path\n",
     "\n",
     "\n",
     "feature_query = FeatureQuery(\n",
@@ -548,7 +548,6 @@
     "                  \"feature_user_totla_purchase_in_90days\",\n",
     "                  \"feature_user_purchasing_power\"], key=user_id)\n",
     "settings = ObservationSettings(\n",
-    "    # observation_path=\"wasbs://public@azurefeathrstorage.blob.core.windows.net/sample_data/green_tripdata_2020-04_with_index.csv\",\n",
     "    observation_path=\"wasbs://public@azurefeathrstorage.blob.core.windows.net/sample_data/product_recommendation_sample/user_observation_mock_data.csv\",\n",
     "    event_timestamp_column=\"event_timestamp\",\n",
     "    timestamp_format=\"yyyy-MM-dd\")\n",
@@ -605,7 +604,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# remove columns\n",
+    "# drop non-feature columns\n",
     "from sklearn.ensemble import GradientBoostingRegressor\n",
     "final_df = df_res\n",
     "final_df.drop([\"event_timestamp\"], axis=1, inplace=True, errors='ignore')\n",
@@ -695,7 +694,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "res = client.get_online_features('productRecommendationDemoFeature', '1', [\n",
+    "client.get_online_features('productRecommendationDemoFeature', '2', [\n",
     "                                 'feature_user_age', 'feature_user_gift_card_balance'])"
    ]
   },
@@ -705,7 +704,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "client.multi_get_online_features(\"productRecommendationFeatureSetting\", [\"1\", \"2\"], [\n",
+    "client.multi_get_online_features('productRecommendationDemoFeature', ['1', '2'], [\n",
     "                                 'feature_user_age', 'feature_user_gift_card_balance'])\n"
    ]
   },