@@ -29,7 +29,7 @@ pip install 'feast[ge]'
 
 ### Dataset profile
 Currently, Feast supports only [Great Expectation's](https://greatexpectations.io/) [ExpectationSuite](https://legacy.docs.greatexpectations.io/en/latest/autoapi/great_expectations/core/expectation_suite/index.html#great_expectations.core.expectation_suite.ExpectationSuite)
-as dataset's profiler. Hence, the user needs to define a function (profiler) that would receive a dataset and return an [ExpectationSuite](https://legacy.docs.greatexpectations.io/en/latest/autoapi/great_expectations/core/expectation_suite/index.html#great_expectations.core.expectation_suite.ExpectationSuite).
+as dataset's profile. Hence, the user needs to define a function (profiler) that would receive a dataset and return an [ExpectationSuite](https://legacy.docs.greatexpectations.io/en/latest/autoapi/great_expectations/core/expectation_suite/index.html#great_expectations.core.expectation_suite.ExpectationSuite).
 
 Great Expectations supports automatic profiling as well as manually specifying expectations:
 ```python
@@ -48,7 +48,7 @@ def automatic_profiler(dataset: Dataset) -> ExpectationSuite:
         value_set_threshold='few'
     ).build_suite()
 ```
-
+However, from our experience capabilities of automatic profiler are quite limited. So we would recommend crafting your own expectations:
 ```python
 @ge_profiler
 def manual_profiler(dataset: Dataset) -> ExpectationSuite:
@@ -59,17 +59,17 @@ def manual_profiler(dataset: Dataset) -> ExpectationSuite:
 
 
 ### Validating Training Dataset
-During retrieval of historical features, `validation_reference` can be passed as a parameter to methods `.to_df(validation_reference=...)` or `.to_arrow(validation_reference=...)`.
-Such job will run validation once dataset is materialized. In case if validation successful materialized dataset is returned.
+During retrieval of historical features, `validation_reference` can be passed as a parameter to methods `.to_df(validation_reference=...)` or `.to_arrow(validation_reference=...)` of RetrievalJob.
+If parameter is provided Feast will run validation once dataset is materialized. In case if validation successful materialized dataset is returned.
 Otherwise, `feast.dqm.errors.ValidationFailed` exception would be raised. It will consist of all details for expectations that didn't pass.
 
 ```python
 from feast import FeatureStore
 
 fs = FeatureStore(".")
 
-fs.get_historical_features(
-    ...,
+job = fs.get_historical_features(...)
+job.to_df(
     validation_reference=fs
         .get_saved_dataset("my_reference_dataset")
         .as_reference(profiler=manual_profiler)
 
@@ -81,6 +81,12 @@ def from_proto(cls, proto: GEValidationProfileProto) -> "GEProfile":
             expectation_suite=ExpectationSuite(**json.loads(proto.expectation_suite))
         )
 
+    def __repr__(self):
+        expectations = json.dumps(
+            [e.to_json_dict() for e in self.expectation_suite.expectations], indent=2
+        )
+        return f"<GEProfile with expectations: {expectations}>"
+
 
 class GEProfiler(Profiler):
     """
 
@@ -83,12 +83,12 @@ def persist(self, storage: SavedDatasetStorage):
 
         if path.endswith(".parquet"):
             pyarrow.parquet.write_table(
-                self._to_arrow_internal(), where=path, filesystem=filesystem
+                self.to_arrow(), where=path, filesystem=filesystem
             )
         else:
             # otherwise assume destination is directory
             pyarrow.parquet.write_to_dataset(
-                self._to_arrow_internal(), root_path=path, filesystem=filesystem
+                self.to_arrow(), root_path=path, filesystem=filesystem
             )
 
     @property
Original file line number	Diff line number	Diff line change
`@@ -83,12 +83,12 @@ def persist(self, storage: SavedDatasetStorage):`
`83`	`83`
`84`	`84`	`if path.endswith(".parquet"):`
`85`	`85`	`pyarrow.parquet.write_table(`
`86`		`- self._to_arrow_internal(), where=path, filesystem=filesystem`
	`86`	`+ self.to_arrow(), where=path, filesystem=filesystem`
`87`	`87`	`)`
`88`	`88`	`else:`
`89`	`89`	`# otherwise assume destination is directory`
`90`	`90`	`pyarrow.parquet.write_to_dataset(`
`91`		`- self._to_arrow_internal(), root_path=path, filesystem=filesystem`
	`91`	`+ self.to_arrow(), root_path=path, filesystem=filesystem`
`92`	`92`	`)`
`93`	`93`
`94`	`94`	`@property`