AIStream-Peelout · isaacmg · Jan 25, 2021 · Jan 29, 2021 · Jan 29, 2021 · Jan 29, 2021
diff --git a/docs/source/dummy_torch.rst b/docs/source/dummy_torch.rst
@@ -1,5 +1,7 @@
 Dummy Torch Model
 ==================
 
+These models were created solely for testing purposes. They provide a known result in order to test logic of train/validation/test loops and functions.
+
 .. automodule:: flood_forecast.transformer_xl.dummy_torch
     :members:
diff --git a/docs/source/evaluator.rst b/docs/source/evaluator.rst
@@ -1,5 +1,7 @@
 Model Evaluation
 =================
 
+Flow Forecast has built in model evaluation capabilities. Model evaluation is called automatically when using the train function.
+
 .. automodule:: flood_forecast.evaluator
     :members:
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -7,7 +7,8 @@ Welcome to Flow Forecast's documentation!
 =========================================
 
 Flow Forecast is a deep learning for time series forecasting framework written in PyTorch. Flow Forecast makes it easy to train PyTorch Forecast models on a wide variety
-of datasets.
+of datasets. Please note that documentation for APIs is provided here. This is good if you want to leverage the underlying Flow Forecast APIs and learn about the specific 
+parameters for models. However, if you are looking for conceptual information, using the configuration files, or benchmarks please vist our `confluence site <https://flow-forecast.atlassian.net/wiki/spaces/FF/overview>`_.
 
 .. automodule:: flood_forecast
 
@@ -60,7 +61,7 @@ of datasets.
 
 .. toctree::
   :maxdepth: 2
-  :caption: TransformerXL:
+  :caption: Transformer Model:
 
   dummy_torch
   lower_upper_config
@@ -81,7 +82,8 @@ of datasets.
 
 .. toctree::
   :maxdepth: 3
-  :caption: DA RNN:
+  :caption: DA-RNN:
+
   model
 
 

diff --git a/docs/source/inference.rst b/docs/source/inference.rst
@@ -1,5 +1,9 @@
 Inference
 =========================
 
+Flow Forecast has a dedicated inference API to make it easy to deploy time series forecasting, classification, and anomaly detection models. Below is the core documentation of
+the inference API. For an authoritative tutorial on using the inference API 
+`please see this notebook <https://colab.research.google.com/drive/1IQO2dBaMqZxN5v9HLQvzlvFLM5__VFZP>`_.
+
 .. automodule:: flood_forecast.deployment.inference
     :members: