diff --git a/doc/nd-structure.md b/doc/nd-structure.md
index b61609870..0b94bbdf8 100644
--- a/doc/nd-structure.md
+++ b/doc/nd-structure.md
@@ -9,15 +9,10 @@ structures. In `kmath` performance depends on which particular context was used
 
 Let us consider following contexts:
 ```kotlin
-    // specialized nd-field for Double. It works as generic Double field as well
-    val specializedField = NDField.real(intArrayOf(dim, dim))
-
     // automatically build context most suited for given type.
-    val autoField = NDField.auto(intArrayOf(dim, dim), RealField)
-
-    //A field implementing lazy computations. All elements are computed on-demand
-    val lazyField = NDField.lazy(intArrayOf(dim, dim), RealField)
-
+    val autoField = NDField.auto(RealField, dim, dim)
+    // specialized nd-field for Double. It works as generic Double field as well
+    val specializedField = NDField.real(dim, dim)
     //A generic boxing field. It should be used for objects, not primitives.
     val genericField = NDField.buffered(intArrayOf(dim, dim), RealField)
 ```
@@ -32,7 +27,7 @@ to it `n = 1000` times.
 The code to run this looks like:
 ```kotlin
     specializedField.run {
-        var res = one
+        var res: NDBuffer<Double> = one
         repeat(n) {
             res += 1.0
         }
@@ -93,7 +88,7 @@ In this case it completes in about `4x-5x` time due to boxing.
 The boxing field produced by
 ```kotlin
     genericField.run {
-        var res = one
+        var res: NDBuffer<Double> = one
         repeat(n) {
             res += 1.0
         }
diff --git a/examples/src/main/kotlin/scientifik/kmath/structures/NDFieldBenchmark.kt b/examples/src/main/kotlin/scientifik/kmath/structures/NDField.kt
similarity index 100%
rename from examples/src/main/kotlin/scientifik/kmath/structures/NDFieldBenchmark.kt
rename to examples/src/main/kotlin/scientifik/kmath/structures/NDField.kt