.

deepeval/dataset/dataset.py

@@ -550,10 +550,16 @@ def push(
         overwrite: Optional[bool] = None,
         auto_convert_test_cases_to_goldens: bool = False,
     ):
-        if len(self.test_cases) == 0 and len(self.goldens) == 0:
-            raise ValueError(
-                "Unable to push empty dataset to Confident AI, there must be at least one test case or golden in dataset"
-            )
+        if auto_convert_test_cases_to_goldens is False:
+            if len(self.goldens) == 0:
+                raise ValueError(
+                    "Unable to push empty dataset to Confident AI, there must be at least one golden in dataset. To include test cases, set 'auto_convert_test_cases_to_goldens' to True."
+                )
+        else:
+            if len(self.test_cases) == 0 and len(self.goldens) == 0:
+                raise ValueError(
+                    "Unable to push empty dataset to Confident AI, there must be at least one test case or golden in dataset"
+                )
         if is_confident():
             goldens = self.goldens
             if auto_convert_test_cases_to_goldens:

docs/docs/confident-ai-evaluation-dataset-management.mdx

@@ -34,16 +34,20 @@ Alternatively, you can also choose to upload entire datasets from CSV files. Sim
 
 Pushing an `EvaluationDataset` on Confident using `deepeval` is a two-step process:
 
-1. Create a dataset locally (same as how you would create a dataset as shown in the [datasets section](evaluation-datasets))
-2. Push the created dataset to Confident
+1. Create a dataset locally (same as how you would create a dataset as shown in the [datasets section](evaluation-datasets)).
+2. Populate it with `Golden`s.
+3. Push the new dataset to Confident AI.
+
+:::warning
+Although you can also populate an `EvaluationDataset` with `LLMTestCase`s, we **HIGHLY** recommend that you do it with `Golden`s instead as it is more flexible to work with when dealing with datasets.
+:::
 
 ### Create A Dataset Locally
 
-Here's a quick example:
+Here's a quick example of populating an `EvaluationDataset` with `Golden`s before pushing it to Confident AI:
 
 ```python
-from deepeval.test_case import LLMTestCase
-from deepeval.dataset import EvaluationDataset
+from deepeval.dataset import EvaluationDataset, Golden
 
 original_dataset = [
     {
@@ -66,44 +70,53 @@ original_dataset = [
     },
 ]
 
-test_cases = []
+goldens = []
 for datapoint in original_dataset:
     input = datapoint.get("input", None)
     actual_output = datapoint.get("actual_output", None)
     expected_output = datapoint.get("expected_output", None)
     context = datapoint.get("context", None)
 
-    test_case = LLMTestCase(
+    golden = Golden(
         input=input,
         actual_output=actual_output,
         expected_output=expected_output,
         context=context
     )
-    test_cases.append(test_case)
+    goldens.append(golden)
 
-dataset = EvaluationDataset(test_cases=test_cases)
+dataset = EvaluationDataset(goldens=goldens)
 ```
 
 ### Push Dataset to Confident AI
 
-After creating your `EvaluationDataset`, all you have to do is push it to Confident by providing an `alias` as an unique identifier:
+After creating your `EvaluationDataset`, all you have to do is push it to Confident by providing an `alias` as an unique identifier. When you push an `EvaluationDataset`, the data is being uploaded as `Golden`s, **NOT** `LLMTestCase`s:
 
 ```python
+...
+
 # Provide an alias when pushing a dataset
 dataset.push(alias="My Confident Dataset")
 ```
 
-:::tip Did you know?
-You can choose to overwrite or append to an existing dataset if an existing dataset with the same alias already exist.
+The `push()` method will upload all `Goldens` found in your dataset to Confident AI, ignoring any `LLMTestCase`s. If you wish to also include `LLMTestCase`s in the push, you can set the `auto_convert_test_cases_to_goldens` parameter to `True`:
 
 ```python
+...
+
+dataset.push(alias="My Confident Dataset", auto_convert_test_cases_to_goldens=True)
+```
+
+You can also choose to overwrite or append to an existing dataset if an existing dataset with the same alias already exist.
+
+```python
+...
+
 dataset.push(alias="My Confident Dataset", overwrite=False)
 ```
 
 `deepeval` will prompt you in the terminal if no value for `overwrite` is provided.
 
-:::
-
 ## What is a Golden?
 
 A "Golden" is what makes up an evaluation dataset and is very similar to a test case in `deepeval`, but they: