diff --git a/GNNLux/docs/make.jl b/GNNLux/docs/make.jl
index f16b716c1..d8016d51d 100644
--- a/GNNLux/docs/make.jl
+++ b/GNNLux/docs/make.jl
@@ -24,7 +24,8 @@ makedocs(;
                    "API Reference" => [
                                     "Basic" => "api/basic.md",
                                     "Convolutional layers" => "api/conv.md",
-                                    "Temporal Convolutional layers" => "api/temporalconv.md",]]
+                                    "Temporal Convolutional layers" => "api/temporalconv.md",],
+                                    ]
          )
          
          
diff --git a/GNNLux/docs/src/index.md b/GNNLux/docs/src/index.md
index 6fa95c3ad..4c72d44f1 100644
--- a/GNNLux/docs/src/index.md
+++ b/GNNLux/docs/src/index.md
@@ -2,4 +2,84 @@
 
 GNNLux.jl is a work-in-progress package that implements stateless graph convolutional layers, fully compatible with the [Lux.jl](https://lux.csail.mit.edu/stable/) machine learning framework. It is built on top of the GNNGraphs.jl, GNNlib.jl, and Lux.jl packages.
 
-The full documentation will be available soon.
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+## Package overview
+
+Let's give a brief overview of the package by solving a graph regression problem with synthetic data. 
+
+### Data preparation
+
+We generate a dataset of multiple random graphs with associated data features, then split it into training and testing sets.
+
+```julia
+using GNNLux, Lux, Statistics, MLUtils, Random
+using Zygote, Optimisers
+
+rng = Random.default_rng()
+
+all_graphs = GNNGraph[]
+
+for _ in 1:1000
+    g = rand_graph(rng, 10, 40,  
+            ndata=(; x = randn(rng, Float32, 16,10)),  # Input node features
+            gdata=(; y = randn(rng, Float32)))         # Regression target   
+    push!(all_graphs, g)
+end
+
+train_graphs, test_graphs = MLUtils.splitobs(all_graphs, at=0.8)
+```
+
+### Model building 
+
+We concisely define our model as a [`GNNLux.GNNChain`](@ref) containing two graph convolutional layers and initialize the model's parameters and state.
+
+```julia
+model = GNNChain(GCNConv(16 => 64),
+                x -> relu.(x),    
+                Dropout(0.6), 
+                GCNConv(64 => 64, relu),
+                x -> mean(x, dims=2),
+                Dense(64, 1)) 
+
+ps, st = LuxCore.setup(rng, model)
+```
+### Training 
+
+Finally, we use a standard Lux training pipeline to fit our dataset.
+
+```julia
+function custom_loss(model, ps, st, tuple)
+    g,x,y = tuple
+    y_pred,st = model(g, x, ps, st)  
+    return MSELoss()(y_pred, y), (layers = st,), 0
+end
+
+function train_model!(model, ps, st, train_graphs, test_graphs)
+    train_state = Lux.Training.TrainState(model, ps, st, Adam(0.0001f0))
+    train_loss=0
+    for iter in 1:100
+        for g in train_graphs
+            _, loss, _, train_state = Lux.Training.single_train_step!(AutoZygote(), custom_loss,(g, g.x, g.y), train_state)
+            train_loss += loss
+        end
+
+        train_loss = train_loss/length(train_graphs)
+
+        if iter % 10 == 0
+            st_ = Lux.testmode(train_state.states)
+            test_loss =0
+            for g in test_graphs
+                ŷ, st_ = model(g, g.x, train_state.parameters, st_)
+                st_ = (layers = st_,)
+                test_loss += MSELoss()(g.y,ŷ)
+            end
+            test_loss = test_loss/length(test_graphs)
+
+            @info (; iter, train_loss, test_loss)
+        end
+    end
+
+    return model, ps, st
+end
+
+train_model!(model, ps, st, train_graphs, test_graphs)
+```
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