diff --git a/docs/source/api/model.rst b/docs/source/api/model.rst
index 6b7e93e2ad..1e674dc208 100644
--- a/docs/source/api/model.rst
+++ b/docs/source/api/model.rst
@@ -7,5 +7,6 @@ Model
    :maxdepth: 2
 
    model/core
-   model/transform
+   model/conditioning
+   model/optimization
    model/fgraph
diff --git a/docs/source/api/model/transform.rst b/docs/source/api/model/conditioning.rst
similarity index 56%
rename from docs/source/api/model/transform.rst
rename to docs/source/api/model/conditioning.rst
index 3e83176b17..c84dceb924 100644
--- a/docs/source/api/model/transform.rst
+++ b/docs/source/api/model/conditioning.rst
@@ -9,12 +9,3 @@ Model Conditioning
    observe
    change_value_transforms
    remove_value_transforms
-
-
-Model Optimization
-------------------
-.. currentmodule:: pymc.model.transform.optimization
-.. autosummary::
-   :toctree: generated/
-
-   freeze_dims_and_data
diff --git a/docs/source/api/model/optimization.rst b/docs/source/api/model/optimization.rst
new file mode 100644
index 0000000000..eb208cd4d2
--- /dev/null
+++ b/docs/source/api/model/optimization.rst
@@ -0,0 +1,7 @@
+Model Optimization
+------------------
+.. currentmodule:: pymc.model.transform.optimization
+.. autosummary::
+   :toctree: generated/
+
+   freeze_dims_and_data
diff --git a/pymc/model/transform/optimization.py b/pymc/model/transform/optimization.py
index bcf828ba3e..187e4ee444 100644
--- a/pymc/model/transform/optimization.py
+++ b/pymc/model/transform/optimization.py
@@ -57,6 +57,28 @@ def freeze_dims_and_data(
     -------
     Model
         A new model with the specified dimensions and data frozen.
+
+
+    Examples
+    --------
+    .. code-block:: python
+
+        import pymc as pm
+        import pytensor.tensor as pt
+
+        from pymc.model.transform.optimization import freeze_dims_and_data
+
+        with pm.Model() as m:
+            x = pm.Data("x", [0, 1, 2] * 1000)
+            y = pm.Normal("y", mu=pt.unique(x).mean())
+
+        # pt.unique(x).mean() has to be computed in every logp function evaluation
+        print("Logp eval time (1000x): ", m.profile(m.logp()).fct_call_time)
+
+        # pt.uniqe(x).mean() is cached in the logp function
+        frozen_m = freeze_dims_and_data(m)
+        print("Logp eval time (1000x): ", frozen_m.profile(frozen_m.logp()).fct_call_time)
+
     """
     fg, memo = fgraph_from_model(model)