diff --git a/docs/source/explicit_comms.rst b/docs/source/explicit_comms.rst
index fe7d0eb7..e075119f 100644
--- a/docs/source/explicit_comms.rst
+++ b/docs/source/explicit_comms.rst
@@ -5,7 +5,7 @@ Communication and scheduling overhead can be a major bottleneck in Dask/Distribu
 The idea is that Dask/Distributed spawns workers and distribute data as usually while the user can submit tasks on the workers that communicate explicitly.
 
 This makes it possible to bypass Distributed's scheduler and write hand-tuned computation and communication patterns. Currently, Dask-CUDA includes an explicit-comms
-implementation of the Dataframe `shuffle <api/#dask_cuda.explicit_comms.dataframe.shuffle.shuffle>`_ operation used for merging and sorting.
+implementation of the Dataframe `shuffle </api/#dask_cuda.explicit_comms.dataframe.shuffle.shuffle>`_ operation used for merging and sorting.
 
 
 Usage
@@ -14,4 +14,4 @@ Usage
 In order to use explicit-comms in Dask/Distributed automatically, simply define the environment variable ``DASK_EXPLICIT_COMMS=True`` or setting the ``"explicit-comms"``
 key in the `Dask configuration <https://docs.dask.org/en/latest/configuration.html>`_.
 
-It is also possible to use explicit-comms in tasks manually, see the `API <api/#explicit-comms>`_ and our `implementation of shuffle <https://github.com/rapidsai/dask-cuda/blob/branch-24.06/dask_cuda/explicit_comms/dataframe/shuffle.py>`_ for guidance.
+It is also possible to use explicit-comms in tasks manually, see the `API </api/#explicit-comms>`_ and our `implementation of shuffle <https://github.com/rapidsai/dask-cuda/blob/branch-24.06/dask_cuda/explicit_comms/dataframe/shuffle.py>`_ for guidance.