diff --git a/docs/about_sceptr.png b/docs/about_sceptr.png
new file mode 100644
index 0000000..fb996c1
Binary files /dev/null and b/docs/about_sceptr.png differ
diff --git a/docs/index.rst b/docs/index.rst
index 730e31b..e02e573 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -11,6 +11,15 @@ And if your computer does have a `CUDA-enabled GPU <https://en.wikipedia.org/wik
 sceptr's :ref:`API <api>` exposes three intuitive functions: :py:func:`~sceptr.calc_vector_representations`, :py:func:`~sceptr.calc_cdist_matrix`, and :py:func:`~sceptr.calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models.
 What's even better is that they are fully compliant with `pyrepseq <https://pyrepseq.readthedocs.io>`_'s `tcr_metric <https://pyrepseq.readthedocs.io/en/latest/api.html#pyrepseq.metric.tcr_metric.TcrMetric>`_ API, so sceptr will fit snugly into the rest of your repertoire analysis toolkit.
 
+.. figure:: about_sceptr.png
+   :width: 700px
+   :alt: Schematic diagrams showing a visual introduction to the architecture of SCEPTR, as well as how it was trained-- namely, autocontrastive learning and masked-language modelling.
+
+   A visual introduction to how SCEPTR works, taken from our SCEPTR preprint.
+   SCEPTR is a TCR language model (a,b) pre-trained using masked-language modelling and autocontrastive learning (c,d).
+   (a) The default model uses the ``<cls>`` pooling method, but there is also a variant that is trained to use average-pooling (see :py:func:`sceptr.variant.average_pooling`).
+   Please see the manuscript for more details.
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents:
diff --git a/src/sceptr/__init__.py b/src/sceptr/__init__.py
index 88537a7..92f9593 100644
--- a/src/sceptr/__init__.py
+++ b/src/sceptr/__init__.py
@@ -45,7 +45,7 @@ def calc_pdist_vector(instances: DataFrame) -> ndarray:
     Returns
     -------
     ndarray
-        A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
+        A 1D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
         The returned array will have shape :math:`(\frac{1}{2}N(N-1),)`, where :math:`N` is the number of TCRs in `instances`.
     """
     return get_default_model().calc_pdist_vector(instances)
diff --git a/src/sceptr/model.py b/src/sceptr/model.py
index 1f7c7fa..23bae7d 100644
--- a/src/sceptr/model.py
+++ b/src/sceptr/model.py
@@ -115,7 +115,7 @@ def calc_pdist_vector(self, instances: DataFrame) -> ndarray:
         Returns
         -------
         ndarray
-            A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
+            A 1D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
             The returned array will have shape :math:`(\frac{1}{2}N(N-1),)`, where :math:`N` is the number of TCRs in `instances`.
         """
         representations = self._calc_torch_representations(instances)