diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index 18e038fd86..97852ce647 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -306,7 +306,17 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
                 (only if multiple val datasets used)
 
         Return:
-            Dict or OrderedDict - passed to the validation_epoch_end
+            Dict or OrderedDict - passed to validation_epoch_end.
+            If you defined validation_step_end it will go to that first.
+
+        .. code-block:: python
+
+            # pseudocode of order
+            out = validation_step()
+            if defined('validation_step_end'):
+                out = validation_step_end(out)
+            out = validation_epoch_end(out)
+
 
         .. code-block:: python
 
@@ -316,7 +326,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
             # if you have multiple val dataloaders:
             def validation_step(self, batch, batch_idx, dataloader_idx)
 
-        EExamples:
+        Examples:
             .. code-block:: python
 
                 # CASE 1: A single validation dataset
@@ -359,12 +369,12 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
         .. note:: When the validation_step is called, the model has been put in eval mode
             and PyTorch gradients have been disabled. At the end of validation,
-            model goes back to training mode and gradients are enabled.
+            the model goes back to training mode and gradients are enabled.
         """
 
     def validation_step_end(self, *args, **kwargs):
         """
-        Use this when training with dp or ddp2 because training_step will operate
+        Use this when validating with dp or ddp2 because validation_step will operate
         on only part of the batch. However, this is still optional
         and only needed for things like softmax or NCE loss.
 
@@ -375,17 +385,14 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
             # pseudocode
             sub_batches = split_batches_for_dp(batch)
-            batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]
+            batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]
             validation_step_end(batch_parts_outputs)
 
         Args:
-            batch_parts_outputs: What you return in `training_step` for each batch part.
+            batch_parts_outputs: What you return in `validation_step` for each batch part.
 
         Return:
-            dictionary with loss key and optional log, progress keys:
-                - loss -> tensor scalar [REQUIRED]
-                - progress_bar -> Dict for progress bar display. Must have only tensors
-                - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
+           Dict or OrderedDict - passed to the validation_epoch_end
 
         In this case you should define validation_step_end to perform those calculations.
 
@@ -394,7 +401,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
                 # WITHOUT validation_step_end
                 # if used in DP or DDP2, this batch is 1/num_gpus large
-                def training_step(self, batch, batch_idx):
+                def validation_step(self, batch, batch_idx):
                     # batch is 1/num_gpus big
                     x, y = batch
 
@@ -405,14 +412,14 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
                 # --------------
                 # with validation_step_end to do softmax over the full batch
-                def training_step(self, batch, batch_idx):
+                def validation_step(self, batch, batch_idx):
                     # batch is 1/num_gpus big
                     x, y = batch
 
                     out = self.forward(x)
                     return {'out': out}
 
-                def validation_step_end(self, outputs):
+                def validation_epoch_end(self, outputs):
                     # this out is now the full size of the batch
                     out = outputs['out']
 
@@ -440,7 +447,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
             val_outs = []
             for val_batch in val_data:
                 out = validation_step(train_batch)
-                train_outs.append(out
+                train_outs.append(out)
             validation_epoch_end(val_outs)
 
         Args:
@@ -516,7 +523,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
             test_outs = []
             for test_batch in test_data:
                 out = test_step(train_batch)
-                test_outs.append(out
+                test_outs.append(out)
             test_epoch_end(test_outs)
 
         Args:
@@ -527,7 +534,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
                 (only if multiple test datasets used)
 
         Return:
-            Dict or OrderedDict - passed to the test_epoch_end
+            Dict or OrderedDict - passed to the test_step_end
 
         .. code-block:: python
 
@@ -579,14 +586,14 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
         .. note:: If you don't need to validate you don't need to implement this method.
 
-        .. note:: When the validation_step is called, the model has been put in eval mode and
-            PyTorch gradients have been disabled. At the end of validation, model goes back
+        .. note:: When the test_step is called, the model has been put in eval mode and
+            PyTorch gradients have been disabled. At the end of the test epoch, the model goes back
             to training mode and gradients are enabled.
         """
 
     def test_step_end(self, *args, **kwargs):
         """
-        Use this when training with dp or ddp2 because training_step will operate
+        Use this when testing with dp or ddp2 because test_step will operate
         on only part of the batch. However, this is still optional
         and only needed for things like softmax or NCE loss.
 
@@ -597,17 +604,14 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
             # pseudocode
             sub_batches = split_batches_for_dp(batch)
-            batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]
+            batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]
             test_step_end(batch_parts_outputs)
 
         Args:
             batch_parts_outputs: What you return in `training_step` for each batch part.
 
         Return:
-            dictionary with loss key and optional log, progress keys:
-                - loss -> tensor scalar [REQUIRED]
-                - progress_bar -> Dict for progress bar display. Must have only tensors
-                - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
+             Dict or OrderedDict - passed to the test_epoch_end
 
         In this case you should define test_step_end to perform those calculations.
 
@@ -616,7 +620,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
                 # WITHOUT test_step_end
                 # if used in DP or DDP2, this batch is 1/num_gpus large
-                def training_step(self, batch, batch_idx):
+                def test_step(self, batch, batch_idx):
                     # batch is 1/num_gpus big
                     x, y = batch
 
@@ -627,7 +631,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
                 # --------------
                 # with test_step_end to do softmax over the full batch
-                def training_step(self, batch, batch_idx):
+                def test_step(self, batch, batch_idx):
                     # batch is 1/num_gpus big
                     x, y = batch
 
@@ -653,7 +657,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
     def test_epoch_end(self, outputs):
         """
-        Called at end of test epoch with the output of all test_steps
+        Called at end of test epoch with the output of all test_steps.
 
         .. code-block:: python
 
@@ -666,7 +670,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
             test_epoch_end(test_outs)
 
         Args:
-
             outputs (list): List of outputs you defined in test_step, or if there are multiple
             dataloaders, a list containing a list of outputs for each dataloader