diff --git a/docs/tutorials/01_getting_started.ipynb b/docs/tutorials/01_getting_started.ipynb
index cb42503..3137882 100644
--- a/docs/tutorials/01_getting_started.ipynb
+++ b/docs/tutorials/01_getting_started.ipynb
@@ -269,7 +269,7 @@
     "\n",
     "Determining the static MPF coefficients for a given set of $k_j$ values amounts to solving a linear system of equations:\n",
     "$Ax=b$, where $x$ are our coefficients of interest, $A$ is a matrix depending on $k_j$ and the type of PF we use ($S$), and $b$ is a vector of constraints.\n",
-    "For brevity, we are not going to go into more detail here and instead refer you to the documentation of [LSE](https://qiskit.github.io/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.LSE.html).\n",
+    "For brevity, we are not going to go into more detail here and instead refer you to the documentation of [LSE](https://qiskit.github.io/qiskit-addon-mpf/apidocs/qiskit_addon_mpf.costs.html#qiskit_addon_mpf.costs.LSE).\n",
     "\n",
     "We can find a solution for $x$ analytically as $x = A^{-1}b$, see e.g. [Carrera Vazquez et al., 2023] or [Zhuk et al., 2023].\n",
     "However, this exact solution can be _\"ill-conditioned\"_ resulting in very large L1-norms of our coefficients, $x$, which can lead to bad performance of the MPF.\n",