diff --git a/docs/source/backends-xnnpack.md b/docs/source/backends-xnnpack.md
index 94287f17c56..e41189d0089 100644
--- a/docs/source/backends-xnnpack.md
+++ b/docs/source/backends-xnnpack.md
@@ -105,7 +105,20 @@ To run the model on-device, use the standard ExecuTorch runtime APIs. See [Runni
 
 The XNNPACK delegate is included by default in the published Android, iOS, and pip packages. When building from source, pass `-DEXECUTORCH_BUILD_XNNPACK=ON` when configuring the CMake build to compile the XNNPACK backend.
 
-To link against the backend, add the `xnnpack_backend` CMake target as a build dependency, or link directly against `libxnnpack_backend`. Due to the use of static registration, it may be necessary to link with whole-archive. This can typically be done by passing the following flags: `-Wl,--whole-archive libxnnpack_backend.a -Wl,--no-whole-archive`.
+To link against the backend, add the `xnnpack_backend` CMake target as a build dependency, or link directly against `libxnnpack_backend`. Due to the use of static registration, it may be necessary to link with whole-archive. This can typically be done by passing `"$<LINK_LIBRARY:WHOLE_ARCHIVE,xnnpack_backend>"` to `target_link_libraries`.
+
+```
+# CMakeLists.txt
+add_subdirectory("executorch")
+...
+target_link_libraries(
+    my_target
+    PRIVATE executorch
+    executorch_module_static
+    executorch_tensor
+    optimized_native_cpu_ops_lib
+    xnnpack_backend)
+```
 
 No additional steps are necessary to use the backend beyond linking the target. Any XNNPACK-delegated .pte file will automatically run on the registered backend.
 
diff --git a/docs/source/using-executorch-cpp.md b/docs/source/using-executorch-cpp.md
index ca81db9a717..4f8a83830e0 100644
--- a/docs/source/using-executorch-cpp.md
+++ b/docs/source/using-executorch-cpp.md
@@ -36,7 +36,7 @@ For more information on the Module class, see [Running an ExecuTorch Model Using
 
 Running a model using the low-level runtime APIs allows for a high-degree of control over memory allocation, placement, and loading. This allows for advanced use cases, such as placing allocations in specific memory banks or loading a model without a file system. For an end to end example using the low-level runtime APIs, see [Running an ExecuTorch Model in C++ Tutorial](running-a-model-cpp-tutorial.md).
 
-## Building with C++
+## Building with CMake
 
 ExecuTorch uses CMake as the primary build system. Inclusion of the module and tensor APIs are controlled by the `EXECUTORCH_BUILD_EXTENSION_MODULE` and `EXECUTORCH_BUILD_EXTENSION_TENSOR` CMake options. As these APIs may not be supported on embedded systems, they are disabled by default when building from source. The low-level API surface is always included. To link, add the `executorch` target as a CMake dependency, along with `executorch_module_static` and `executorch_tensor`, if desired.