diff --git a/docsite/docs/guides/integration.ipynb b/docsite/docs/guides/integration.ipynb
index c1c3554..6f9419d 100644
--- a/docsite/docs/guides/integration.ipynb
+++ b/docsite/docs/guides/integration.ipynb
@@ -140,7 +140,7 @@
     }
    ],
    "source": [
-    "from typing import Optional\n",
+    "from typing import Iterable, List, Optional\n",
     "\n",
     "from sklearn.base import BaseEstimator, TransformerMixin\n",
     "from sklearn.pipeline import Pipeline\n",
@@ -158,7 +158,7 @@
     "\n",
     "    def fit(self, X, y = None):\n",
     "        \"\"\"\n",
-    "        Generate the initial model spec by which subsequent X's will be \n",
+    "        Generate the initial model spec by which subsequent X's will be\n",
     "        transformed.\n",
     "        \"\"\"\n",
     "        self.model_spec = self.formula.get_model_matrix(X).model_spec\n",
@@ -174,6 +174,14 @@
     "        X_ = self.model_spec.get_model_matrix(X)\n",
     "        return X_\n",
     "\n",
+    "    def get_feature_names_out(self, input_features: Optional[Iterable[str]] = None) -> List[str]:\n",
+    "        \"\"\"\n",
+    "        Expose model spec column names to scikit learn to allow column transforms later in the pipeline.\n",
+    "        \"\"\"\n",
+    "        if self.model_spec is None:\n",
+    "            raise RuntimeError(\"`FormulaicTransformer.fit()` must be called before columns can be assigned names.\")\n",
+    "        return self.model_spec.column_names\n",
+    "\n",
     "\n",
     "pipe = Pipeline([\n",
     "    (\"formula\", FormulaicTransformer(\"x1 + x2 + x3\")),\n",