diff --git a/example/print-sample-input-file.f90 b/example/print-sample-input-file.f90
deleted file mode 100644
index b8cc6922a..000000000
--- a/example/print-sample-input-file.f90
+++ /dev/null
@@ -1,24 +0,0 @@
-program print_sample_input_file
-  use inference_engine_m, only : hyperparameters_t, network_configuration_t
-  implicit none
-
-  associate(params => hyperparameters_t(mini_batches=10, learning_rate=1.5, optimizer = "adam"))
-    associate(net_conf=> network_configuration_t(skip_connections=.false., nodes_per_layer=[2,72,2], activation_function="sigmoid"))
-      associate(params_json => params%to_json(), net_json => net_conf%to_json())
-        print *,"{"
-        block 
-          integer line
-          do line = 1, size(params_json)
-            print *, (params_json(line)%string())
-          end do
-          do line = 1, size(net_json)
-            print *, (net_json(line)%string())
-          end do
-        end block
-        print *,"}"
-      end associate
-    end associate
-  end associate
-
-
-end program
diff --git a/example/print-training-configuration.f90 b/example/print-training-configuration.f90
new file mode 100644
index 000000000..c002286c2
--- /dev/null
+++ b/example/print-training-configuration.f90
@@ -0,0 +1,15 @@
+program print_training_configuration
+  !! Demonstrate how to construct and print a training_configuration_t object
+  use inference_engine_m, only : training_configuration_t, hyperparameters_t, network_configuration_t
+  use sourcery_m, only : file_t
+  implicit none
+
+  associate(training_configuration => training_configuration_t( &
+    hyperparameters_t(mini_batches=10, learning_rate=1.5, optimizer = "adam"), &
+    network_configuration_t(skip_connections=.false., nodes_per_layer=[2,72,2], activation_function="sigmoid") &
+  ))
+    associate(json_file => file_t(training_configuration%to_json()))
+      call json_file%write_lines()
+    end associate
+  end associate
+end program