diff --git a/docs/workflow/reuse.rst b/docs/workflow/reuse.rst
index c505f3d..5f5a3f5 100644
--- a/docs/workflow/reuse.rst
+++ b/docs/workflow/reuse.rst
@@ -3,10 +3,13 @@ Getting Started-Workflow-Reuse learnware
 ==========================================
 
 This part introduces two baseline methods for reusing a given list of learnwares, namely ``JobSelectorReuser`` and ``AveragingReuser``.
-Instead of training a model from scratch, you can easily reuse a list of learnwares ``learnware_list (List[Learnware])`` to make predictions on your own data ``test_data (numpy.ndarray or torch.Tensor)`` in the following way:
+Instead of training a model from scratch, the user can easily reuse a list of learnwares (``List[Learnware]``) to predict the labels of their own data (``numpy.ndarray`` or ``torch.Tensor``).
+
+To illustrate, we provide a code demonstration that obtains the user dataset using ``sklearn.datasets.load_digits``, where ``test_data`` represents the data that requires prediction.
+Assuming that ``learnware_list`` is the list of learnwares searched by the learnware market based on user specifications, the user can reuse each learnware in the ``learnware_list`` through ``JobSelectorReuser`` or ``AveragingReuser`` to predict the label of ``test_data``, thereby avoiding training a model from scratch.
 
 .. code-block:: python
-    
+
     from sklearn.datasets import load_digits
     from learnware.learnware import JobSelectorReuser, AveragingReuser
 
@@ -35,7 +38,7 @@ There are three parameters required to initialize the class:
 - ``herding_num``: An optional integer that specifies the number of items to herd, which defaults to 1000 if not provided.
 - ``use_herding``: A boolean flag indicating whether to use kernel herding.
 
-The job selector is essentially a multi-class classifier :math:`g(\boldsymbol{x}\rightarrow \mathcal{I})` with :math:`\mathcal{I}=\{1,\ldots, C\}`.
+The job selector is essentially a multi-class classifier :math:`g(\boldsymbol{x}):\mathcal{X}\rightarrow \mathcal{I}` with :math:`\mathcal{I}=\{1,\ldots, C\}`, where :math:`C` is the size of ``learnware_list``.
 Given a testing sample :math:`\boldsymbol{x}`, the ``JobSelectorReuser`` predicts it by using the :math:`g(\boldsymbol{x})`-th learnware in ``learnware_list``.
 If ``use_herding`` is set to false, the ``JobSelectorReuser`` uses data points in each learware's RKME spefication with the corresponding learnware index to train a job selector.
 If ``use_herding`` is true, the algorithm estimates the mixture weight based on RKME specifications and raw user data, uses the weight to generate ``herding_num`` auxiliary data points mimicking the user distribution through the kernel herding method, and learns a job selector on these data.