diff --git a/Run TCAV.ipynb b/Run TCAV.ipynb
index e2a7634f0f..ee314c8ec8 100644
--- a/Run TCAV.ipynb	
+++ b/Run TCAV.ipynb	
@@ -14,7 +14,7 @@
     " * images for the class/labels of interest\n",
     " * random images that will be negative examples when learning CAVs (images that probably don't belong to any concepts)\n",
     "2. **model wrapper**: an instance of  ModelWrapper abstract class (in model.py). This tells TCAV class (tcav.py) how to communicate with your model (e.g., getting internal tensors)\n",
-    "3. **act_generator**: an instance of ActivationGeneratorInterface that tells TCAV class how to get activations from the model\n",
+    "3. **act_generator**: an instance of ActivationGeneratorInterface that tells TCAV class how to load example data and how to get activations from the model\n",
     "\n",
     "\n",
     "\n",
@@ -210,7 +210,7 @@
    "source": [
     "## Step 3. Implement a class that returns activations (maybe with caching!)\n",
     "\n",
-    "Lastly, you will implement a class of the ActivationGenerationInterface which TCAV uses to call into your model wrapper and return activations. I pulled out this function outside of mymodel because this step often takes the longest. By making it modular, you can cache your activations and/or parallelize your computations, as I have done in `ActivationGeneratorBase.process_and_load_activations` in `activation_generator.py`.\n",
+    "Lastly, you will implement a class of the ActivationGenerationInterface which TCAV uses to load example data for a given concept or target, call into your model wrapper and return activations. I pulled out this logic outside of mymodel because this step often takes the longest. By making it modular, you can cache your activations and/or parallelize your computations, as I have done in `ActivationGeneratorBase.process_and_load_activations` in `activation_generator.py`.\n",
     "\n",
     "\n",
     "The `process_and_load_activations` method of the activation generator must return a dictionary of activations that has concept or target name as  a first key, and the bottleneck name as a second key. So something like:\n",