diff --git a/doc/design/scope.md b/doc/design/scope.md
index b0ee74453586dae6d3d61c60a4cb2d97eeaceb73..de2e67d327224c95b5f50a23e0171242e19d0073 100644
--- a/doc/design/scope.md
+++ b/doc/design/scope.md
@@ -31,11 +31,38 @@ class Scope {
 };
 ```
 
-## Scope 可以被继承或者叫共享
 
-解释下Scope如何被共享，如何查找Variable的算法。
-       * Scope永远从本地寻找Variable，找不到会从他的父亲Scope寻找Variable
-    * 嵌套深度不做要求。
+## Parent scope and local scope
+
+Just like [scope](https://en.wikipedia.org/wiki/Scope_(computer_science)) in programming languages, `Scope` in the neural network can also be a local scope. There are two attributes about local scope.
+
+1.  We can create local variables in a local scope. When that local scope are destroyed, all local variables should also be destroyed.
+2.  Variables in a parent scope can be retrieved from local scopes of that parent scope, i.e., when user get a variable from a scope, it will try to search this variable in current scope. If there is no such variable in the local scope, `scope` will keep searching from its parent, until the variable is found or there is no parent.
+
+```cpp
+class Scope {
+public:
+  Scope(const std::shared_ptr<Scope>& scope): parent_(scope) {}
+
+  Variable* Get(const std::string& name) const {
+    Variable* var = GetVarLocally(name);
+    if (var != nullptr) {
+      return var;
+    } else if (parent_ != nullptr) {
+      return parent_->Get(name);
+    } else {
+      return nullptr;
+    }
+  }
+
+private:
+  std::shared_ptr<Scope> parent_ {nullptr};
+};
+```
+
+In `Scope` class, there is a private data member called `parent_`. `parent_` is a smart pointer to its parent scope. When user `Get` a variable by its `name`, the `name` will be searched inside the current scope. If the variable cannot be found locally and parent scope is not a `nullptr`, the variable will be searched inside that parent scope. `parent_` pointer's default value is `nullptr`. It means that the scope is a global scope when `parent_` is nullptr.
+
+A local scope is very useful when we implement Recurrent Neural Network. Each timestep of an RNN should be a `Net`. Each `Net` of timestep (`StepNet` for short) should use an independent local scope. Just like variables in a while loop is inside a local scope in programming languages. By using a single `StepNet` and changing local scope, we can implement an RNN easily.
 
 # 接口实现