diff --git a/examples/tnt-llm/img/tnt_llm.png b/examples/tnt-llm/img/tnt_llm.png
new file mode 100644
index 000000000..dbe96813e
Binary files /dev/null and b/examples/tnt-llm/img/tnt_llm.png differ
diff --git a/examples/tnt-llm/tnt-llm.ipynb b/examples/tnt-llm/tnt-llm.ipynb
index 365db10ea..ed5a2d264 100644
--- a/examples/tnt-llm/tnt-llm.ipynb
+++ b/examples/tnt-llm/tnt-llm.ipynb
@@ -18,7 +18,9 @@
     "3. Finetune classifier + deploy\n",
     "\n",
     "\n",
-    "In this notebook, we will focus on the first phase: taxonomy generation.\n",
+    "In this notebook, we will focus on the first phase: taxonomy generation (blue in the diagram below).\n",
+    "\n",
+    "![TNT LLM Diagram](./img/tnt_llm.png)\n",
     "\n",
     "To generate the taxonomy, TNT-LLM proposes 5 steps:\n",
     "\n",
@@ -612,24 +614,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "c2340177-f40c-407a-8e3e-cb06c2ef09ce",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "summarize {'documents': [{'id' ...\n",
-      "get_minibatches {'minibatches': [[39 ...\n",
-      "generate_taxonomy {'clusters': [[{'id' ...\n",
-      "update_taxonomy {'clusters': [[{'id' ...\n",
-      "update_taxonomy {'clusters': [[{'id' ...\n",
-      "review_taxonomy {'clusters': [[{'id' ...\n",
-      "__end__ {'documents': [{'id' ...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We will randomly sample down to 1K docs to speed things up\n",
     "docs = [run_to_doc(run) for run in runs if run.inputs]\n",
@@ -640,28 +628,31 @@
     "    \" that would benefit the user.\"\n",
     ")\n",
     "\n",
-    "stream = app.stream(\n",
-    "    {\"documents\": docs},\n",
-    "    {\n",
-    "        \"configurable\": {\n",
-    "            \"use_case\": use_case,\n",
-    "            # Optional:\n",
-    "            \"batch_size\": 400,\n",
-    "            \"suggestion_length\": 30,\n",
-    "            \"cluster_name_length\": 10,\n",
-    "            \"cluster_description_length\": 30,\n",
-    "            \"explanation_length\": 20,\n",
-    "            \"max_num_clusters\": 25,\n",
-    "        },\n",
-    "        # We batch summarize the docs. To avoid getting errors, we will limit the\n",
-    "        # degree of parallelism to permit.\n",
-    "        \"max_concurrency\": 2,\n",
-    "    },\n",
-    ")\n",
+    "from langchain_core.tracers.context import tracing_v2_enabled\n",
     "\n",
-    "for step in stream:\n",
-    "    node, state = next(iter(step.items()))\n",
-    "    print(node, str(state)[:20] + \" ...\")"
+    "with tracing_v2_enabled(client=Client(api_key=\"ls__eaec6db115fe4ad2af4fdf26fa553645\")):\n",
+    "    stream = app.stream(\n",
+    "        {\"documents\": docs},\n",
+    "        {\n",
+    "            \"configurable\": {\n",
+    "                \"use_case\": use_case,\n",
+    "                # Optional:\n",
+    "                \"batch_size\": 400,\n",
+    "                \"suggestion_length\": 30,\n",
+    "                \"cluster_name_length\": 10,\n",
+    "                \"cluster_description_length\": 30,\n",
+    "                \"explanation_length\": 20,\n",
+    "                \"max_num_clusters\": 25,\n",
+    "            },\n",
+    "            # We batch summarize the docs. To avoid getting errors, we will limit the\n",
+    "            # degree of parallelism to permit.\n",
+    "            \"max_concurrency\": 2,\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "    for step in stream:\n",
+    "        node, state = next(iter(step.items()))\n",
+    "        print(node, str(state)[:20] + \" ...\")"
    ]
   },
   {