diff --git a/docs/examples/models_from_typeddict.py b/docs/examples/models_from_typeddict.py
new file mode 100644
index 0000000000..54136274e9
--- /dev/null
+++ b/docs/examples/models_from_typeddict.py
@@ -0,0 +1,21 @@
+from typing import TypedDict
+
+from pydantic import ValidationError, create_model_from_typeddict
+
+
+class User(TypedDict):
+    name: str
+    id: int
+
+
+class Config:
+    extra = 'forbid'
+
+
+UserM = create_model_from_typeddict(User, __config__=Config)
+print(repr(UserM(name=123, id='3')))
+
+try:
+    UserM(name=123, id='3', other='no')
+except ValidationError as e:
+    print(e)
diff --git a/docs/usage/models.md b/docs/usage/models.md
index 87312469db..062808f6f5 100644
--- a/docs/usage/models.md
+++ b/docs/usage/models.md
@@ -384,6 +384,18 @@ You can also add validators by passing a dict to the `__validators__` argument.
 {!.tmp_examples/models_dynamic_validators.py!}
 ```
 
+## Model creation from `NamedTuple` or `TypedDict`
+
+Sometimes you already use in your application classes that inherit from `NamedTuple` or `TypedDict`
+and you don't want to duplicate all your information to have a `BaseModel`.
+For this _pydantic_ provides `create_model_from_namedtuple` and `create_model_from_typeddict` methods.
+Those methods have the exact same keyword arguments as `create_model`.
+
+
+```py
+{!.tmp_examples/models_from_typeddict.py!}
+```
+
 ## Custom Root Types
 
 Pydantic models can be defined with a custom root type by declaring the `__root__` field.