diff --git a/imperative/python/megengine/functional/nn.py b/imperative/python/megengine/functional/nn.py
index bfa3d9b867f69076574485ad67a8bb1b2f905c9f..7caefe90e76c55f9bb7c9f8ffd360b6a161aab26 100644
--- a/imperative/python/megengine/functional/nn.py
+++ b/imperative/python/megengine/functional/nn.py
@@ -16,7 +16,7 @@ from ..core.ops import builtin
 from ..core.ops.builtin import BatchNorm
 from ..core.ops.special import Const
 from ..core.tensor import megbrain_graph, utils
-from ..core.tensor.utils import astensor1d
+from ..core.tensor.utils import astensor1d, setscalar
 from ..distributed import WORLD, is_distributed
 from ..jit.tracing import is_tracing
 from ..random import uniform
@@ -1133,7 +1133,8 @@ def matmul(
 def dot(inp1: Tensor, inp2: Tensor) -> Tensor:
     """
     Computes dot-product of two vectors ``inp1`` and ``inp2``.
-    inputs must be 1-dimensional, scalar input can be automatically broadcasted.
+    inputs must be 1-dimensional or scalar. A scalar input is automatically broadcasted.
+    Refer to :func:`~.matmul` for more general usage.
 
     :param inp1: first vector.
     :param inp2: second vector.
@@ -1156,12 +1157,16 @@ def dot(inp1: Tensor, inp2: Tensor) -> Tensor:
 
     .. testoutput::
 
-        [55.]
+        55.
 
     """
     op = builtin.Dot()
     inp1, inp2 = utils.convert_inputs(inp1, inp2)
+    assert (
+        inp1.ndim <= 1 and inp2.ndim <= 1
+    ), "Input tensors for dot must be 1-dimensional or scalar"
     (result,) = apply(op, inp1, inp2)
+    setscalar(result)
     return result