diff --git a/ttnn/cpp/ttnn/operations/binary.hpp b/ttnn/cpp/ttnn/operations/binary.hpp
index 8c22db4bbc4..85da18142da 100644
--- a/ttnn/cpp/ttnn/operations/binary.hpp
+++ b/ttnn/cpp/ttnn/operations/binary.hpp
@@ -47,7 +47,7 @@ inline ttnn::Tensor add(
 
     if (height_b == 1 or width_b == 1) {
                 if (dtype.has_value()) {
-                    TT_THROW("Bcast: Unsupported dtype argument.");
+                    TT_THROW("ttnn.add: cannot change dtype when broadcasting");
                 }
         tt::tt_metal::BcastOpDim bcast_op_dim;
         if (height_b == 1 and width_b == 1) {
diff --git a/ttnn/ttnn/operations/core.py b/ttnn/ttnn/operations/core.py
index befd945f8e3..25f2039da9d 100644
--- a/ttnn/ttnn/operations/core.py
+++ b/ttnn/ttnn/operations/core.py
@@ -814,7 +814,8 @@ def clone(tensor, memory_config: ttnn.MemoryConfig, dtype: ttnn.DataType):
     In the case where the layout is the same, the operation simply pad or unpad the last two dimensions depending on layout requested.
     Args:
         * :attr:`tensor`: the ttnn.Tensor
-        * :attr:`layout`: the layout of either ttnn.ROW_MAJOR_LAYOUT or ttnn.TILE_LAYOUT.
+        * :attr:`memory_config`: the `ttnn` memory config, DRAM_MEMORY_CONFIG or L1_MEMORY_CONFIG.
+        * :attr:`dtype`: the `ttnn` data type.
     Example::
         >>> tensor = ttnn.to_device(ttnn.from_torch(torch.zeros((1, 1, 64, 32), dtype=torch.bfloat16, layout=ttnn.TILE_LAYOUT)), device, memory_config=ttnn.DRAM_MEMORY_CONFIG)
         >>> output = ttnn.clone(tensor, tnn.DRAM_MEMORY_CONFIG, tnn.bfloat8_b)