diff --git a/docs/source/introduction_guide.rst b/docs/source/introduction_guide.rst
index c3dbb57e..765ba8fd 100644
--- a/docs/source/introduction_guide.rst
+++ b/docs/source/introduction_guide.rst
@@ -494,13 +494,13 @@ In this method we do all the preparation we need to do once (instead of on every
         self.test_dataset = mnist_test
 
       def train_dataloader(self):
-        return DataLoader(train_dataset, batch_size=64)
+        return DataLoader(self.train_dataset, batch_size=64)
 
       def val_dataloader(self):
-        return DataLoader(mnist_val, batch_size=64)
+        return DataLoader(self.mnist_val, batch_size=64)
 
       def test_dataloader(self):
-        return DataLoader(mnist_test, batch_size=64)
+        return DataLoader(self.mnist_test, batch_size=64)
 
 The `prepare_data` method is also a good place to do any data processing that needs to be done only
 once (ie: download or tokenize, etc...).
diff --git a/pytorch_lightning/core/__init__.py b/pytorch_lightning/core/__init__.py
index 39479cf2..fac76ffd 100644
--- a/pytorch_lightning/core/__init__.py
+++ b/pytorch_lightning/core/__init__.py
@@ -236,11 +236,11 @@ Data preparation
 ----------------
 Data preparation in PyTorch follows 5 steps:
 
-1. Download
-2. Clean and (maybe) save to disk
-3. Load inside dataset
-4. Apply transforms (rotate, tokenize, etc...)
-5. Wrap inside a dataloader
+    1. Download
+    2. Clean and (maybe) save to disk
+    3. Load inside dataset
+    4. Apply transforms (rotate, tokenize, etc...)
+    5. Wrap inside a dataloader
 
 When working in distributed settings, steps 1 and 2 have to be done
 from a single GPU, otherwise you will overwrite these files from
@@ -251,8 +251,10 @@ allow for this
 
     def prepare_data(self):
         # download
-        mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())
-        mnist_test = MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor())
+        mnist_train = MNIST(os.getcwd(), train=True, download=True,
+                            transform=transforms.ToTensor())
+        mnist_test = MNIST(os.getcwd(), train=False, download=True,
+                            transform=transforms.ToTensor())
 
         # train/val split
         mnist_train, mnist_val = random_split(mnist_train, [55000, 5000])
@@ -263,13 +265,13 @@ allow for this
         self.test_dataset = mnist_test
 
       def train_dataloader(self):
-        return DataLoader(train_dataset, batch_size=64)
+        return DataLoader(self.train_dataset, batch_size=64)
 
       def val_dataloader(self):
-        return DataLoader(mnist_val, batch_size=64)
+        return DataLoader(self.mnist_val, batch_size=64)
 
       def test_dataloader(self):
-        return DataLoader(mnist_test, batch_size=64)
+        return DataLoader(self.mnist_test, batch_size=64)
 
 .. note:: ``prepare_data`` is called once.
 
@@ -305,6 +307,9 @@ Check out this
 `COLAB <https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg>`_
 for a live demo.
 
+LightningModule Class
+---------------------
+
 """
 
 from .decorators import data_loader