diff --git a/tutorial/source/index.rst b/tutorial/source/index.rst
index 0c4f0153c2..f8fa99ad37 100644
--- a/tutorial/source/index.rst
+++ b/tutorial/source/index.rst
@@ -158,6 +158,7 @@ List of Tutorials
    stable
    forecast_simple
    timeseries
+   reconciling_experts
 
 .. toctree::
    :maxdepth: 1
diff --git a/tutorial/source/reconciling_experts.ipynb b/tutorial/source/reconciling_experts.ipynb
index eeab990dc8..6752c57ad6 100644
--- a/tutorial/source/reconciling_experts.ipynb
+++ b/tutorial/source/reconciling_experts.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Reconciling Experts\n",
+    "# Example: Reconciling Expert Opinions\n",
     "\n",
     "This tutorial demonstrates how time dependent expert opinions can be reconciled in a Bayesian framework. We make use of the following effect handlers in order to modify model behavior:\n",
     "- [poutine.reparam](http://docs.pyro.ai/en/latest/poutine.html#pyro.poutine.handlers.reparam) for splitting observations to several random variables using the [SplitReparam](https://docs.pyro.ai/en/latest/infer.reparam.html#pyro.infer.reparam.split.SplitReparam) reparameterizer.\n",
@@ -13,7 +13,8 @@
     "\n",
     "## Summary\n",
     "- Effect handlers are useful in modifying model behavior, beyond the straightforward use of effect handlers in order to condition models on known observations. \n",
-    "- Directly sampling from modified models is often not possible, and one should resort to inference methods such as SVI or MCMC in order to sample from models modified by effect handlers. This is true even if the models are not conditioned on known observations.\n",
+    "- Directly sampling from modified models is often not possible, and one should resort to inference methods such as SVI or MCMC in order to sample from models modified by effect handlers.\n",
+    "- The above is true even if the models are not conditioned on known observations.\n",
     "\n",
     "\n",
     "## Some Preliminaries\n",