diff --git a/doc/user_guide/large_datasets.rst b/doc/user_guide/large_datasets.rst
index 0269be6a0..b5ee0a232 100644
--- a/doc/user_guide/large_datasets.rst
+++ b/doc/user_guide/large_datasets.rst
@@ -12,7 +12,7 @@ If you try to create a plot that will directly embed a dataset with more than
    import pandas as pd
 
    data = pd.DataFrame({"x": range(10000)})
-   alt.Chart(data).mark_line()
+   alt.Chart(data).mark_point()
 
 .. code-block:: none
 
@@ -336,15 +336,15 @@ summary statistics to Altair instead of the full dataset.
 VegaFusion
 ~~~~~~~~~~
 `VegaFusion`_ is a third-party package that re-implements most Vega-Lite transforms for evaluation
-in the Python kernel.  This makes it possible to scale many Altair charts to millions of rows as long as
-they include some form of aggregation.
-
-VegaFusion 1.0 provides two rendering modes that are useful in different situations.
+in the Python kernel, which makes it possible to scale Altair charts to millions of rows of data.
+VegaFusion provides two rendering modes that are useful in different situations.
 
 Mime Renderer
 ^^^^^^^^^^^^^
-The `VegaFusion mime renderer`_ is a good choice for charts that do not re-aggregate or re-filter data in response
-to selections. It is enabled with:
+The `VegaFusion mime renderer`_ is a good choice for charts that contain aggregations
+and that do not re-aggregate or re-filter data in response to selections
+(so it offers similar but more advanced functionality as the ``altair-transform`` package).
+It is enabled with:
 
 .. code-block:: python
 
@@ -357,23 +357,28 @@ Python kernel. It then removes any unused columns and inlines the transformed da
 for rendering.
 
 Charts rendered this way are self-contained and do not require the Python kernel or a custom
-notebook extension to display.  They are rendered with the same frontend functionality that
+notebook extension to display. They are rendered with the same frontend functionality that
 is already used to display regular Altair charts.
 
 Widget Renderer
 ^^^^^^^^^^^^^^^
-The `VegaFusion widget renderer`_ is a good choice for charts that re-aggregate or re-filter data in response
-to selections. It is enabled with:
+The `VegaFusion widget renderer`_ is a good choice for displaying unaggregated data
+and for aggregated charts that re-aggregate or re-filter data in response to selections
+(so it offers similar but more advanced functionality as the ``altair-data-server`` package).
+It is enabled with:
 
 .. code-block:: python
 
     import vegafusion as vf
     vf.enable_widget()
 
-The widget renderer uses a Jupyter Widget extension to maintain a live connection between the displayed
-chart and the Python kernel. This makes it possible for transforms to be evaluated interactively in response to
-changes in selections. Charts rendered this way require a running Python kernel and Jupyter Widget extension to
-display.
+The widget renderer uses a Jupyter Widget extension to maintain a live connection between the displayed chart and the Python kernel.
+This makes it possible for transforms to be evaluated interactively in response to changes in selections,
+and to send the datasets to the client in arrow format separately instead of inlining them in the chart json spec. 
+
+Charts rendered this way require a running Python kernel and Jupyter Widget extension to
+display, which works in many frontends including locally in the classic notebook, JupyterLab, and VSCode,
+as well as remotely in Colab and Binder.
 
 .. _VegaFusion: https://vegafusion.io
 .. _VegaFusion mime renderer: https://vegafusion.io/mime_renderer.html