diff --git a/README.md b/README.md
index ea36828f..f8204d1b 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,7 @@ To use lightning do 2 things:
 2. [Define a LightningModel](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py).     
 
 ## What does lightning control for me?
-Everything! Except the following three things:
+Everything! Except you define your data and what happens inside the training and validation loop.   
 
 **What happens in the training loop**    
 
@@ -46,8 +46,22 @@ def training_step(self, data_batch, batch_nb):
     x, y = data_batch
     
     # define your own forward and loss calculation
-    out = self.forward(x)
-    loss = my_loss(out, y)
+    hidden_states = self.encoder(x)
+     
+    # even as complex as a seq-2seq + attn model
+    # (this is just a toy, non-working example to illustrate)
+    start_token = '<SOS>'
+    last_hidden = torch.zeros(...)
+    loss = 0
+    for step in range(max_seq_len):
+        attn_context = self.attention_nn(hidden_states, start_token)
+        pred = self.decoder(start_token, attn_context, last_hidden) 
+        last_hidden = pred
+        pred = self.predict_nn(pred)
+        loss += self.loss(last_hidden, y[step])
+        
+    #toy example as well
+    loss = loss / max_seq_len
     return {'loss': loss} 
 ```
 
@@ -58,7 +72,7 @@ def training_step(self, data_batch, batch_nb):
 def validation_step(self, data_batch, batch_nb):    
     x, y = data_batch
     
-    # define your own forward and loss calculation
+    # or as basic as a CNN classification
     out = self.forward(x)
     loss = my_loss(out, y)
     return {'loss': loss}