diff --git a/docs/src/basics/layers.md b/docs/src/basics/layers.md
index 041d7a3ec..079e25372 100644
--- a/docs/src/basics/layers.md
+++ b/docs/src/basics/layers.md
@@ -7,7 +7,20 @@ model = Chain(GCNConv(adj_mat, feat=>h1),
               GCNConv(adj_mat, h1=>h2, relu))
 ```
 
-`GCNConv` is used for layer construction for neural network. The first argument `adj_mat` is the representation of a graph in form of adjacency matrix. The feature dimension in first layer is mapped from `feat` to `h1`. In second layer, `h1` is then mapped to `h2`. Default activation function is given as identity if it is not specified by users.
+In the example above, The first argument `adj_mat` is the representation of a graph in form of adjacency matrix. The feature dimension in first layer is mapped from `feat` to `h1`. In second layer, `h1` is then mapped to `h2`. Default activation function is given as identity if it is not specified by users.
+
+The initialization function `GCNConv(...)` constructs a `GCNConv` layer. For most of the layer types in GeometricFlux, a layer can be initialized in at least two ways:
+
+* Initializing *with* a predefined adjacency matrix or `FeaturedGraph`, followed by the other parameters. For most of the layer types, this is for datasets where each input has the same graph structure.
+* Initializing *without* an initial graph argument, only supplying the relevant parameters. This allows the layer to accept different graph structures.
+
+# Applying layers
+
+When using GNN layers, the general guidelines are:
+
+* If you pass in a ``n \times d`` matrix of node features, and the layer maps node features ``\mathbb{R}^d \rightarrow \mathbb{R}^k`` then the output will be in matrix with dimensions ``n \times k``. The same ostensibly goes for edge features but as of now no layer type supports outputting new edge features.
+* If you pass in a `FeaturedGraph`, the output will be also be a `FeaturedGraph` with modified node (and/or edge) features. Add `node_feature` as the following entry in the Flux chain (or simply call `node_feature()` on the output) if you wish to subsequently convert them to matrix form.
+
 
 ## Customize layers