diff --git a/docs/source/examples/best-practices.rst b/docs/source/examples/best-practices.rst
index fbfd8f0c..76d3701f 100644
--- a/docs/source/examples/best-practices.rst
+++ b/docs/source/examples/best-practices.rst
@@ -49,8 +49,8 @@ Spilling from Device
 
 Dask-CUDA offers several different ways to enable automatic spilling from device memory.
 The best method often depends on the specific workflow. For classic ETL workloads using
-`Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_, cuDF spilling is usually the
-best place to start. See :ref:`Spilling from device <spilling-from-device>` for more details.
+`Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_, native cuDF spilling is usually
+the best place to start. See :ref:`Spilling from device <spilling-from-device>` for more details.
 
 Accelerated Networking
 ~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/source/spilling.rst b/docs/source/spilling.rst
index cfc6cfcf..037b193b 100644
--- a/docs/source/spilling.rst
+++ b/docs/source/spilling.rst
@@ -114,7 +114,7 @@ cuDF Spilling
 
 When executing an ETL workflow with `Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_
 (i.e. Dask DataFrame), it is usually best to leverage `native spilling support in cuDF
-<https://docs.rapids.ai/api/cudf/stable/developer_guide/library_design/#spilling-to-host-memory>`.
+<https://docs.rapids.ai/api/cudf/stable/developer_guide/library_design/#spilling-to-host-memory>_`.
 
 Native cuDF spilling has an important advantage over the other methodologies mentioned
 above. When JIT-unspill or default spilling are used, the worker is only able to spill