diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb
index 72b65c6ca9..b9ede4263e 100644
--- a/docs/backend/native_api.ipynb
+++ b/docs/backend/native_api.ipynb
@@ -371,7 +371,9 @@
    "source": [
     "## Capture expert selection distribution in MoE models\n",
     "\n",
-    "SGLang Runtime supports recording the number of times an expert is selected in a MoE model run for each expert in the model. This is useful when analyzing the throughput of the model and plan for optimization."
+    "SGLang Runtime supports recording the number of times an expert is selected in a MoE model run for each expert in the model. This is useful when analyzing the throughput of the model and plan for optimization.\n",
+    "\n",
+    "*Note: We only print out the first 10 lines of the csv below for better readability. Please adjust accordingly if you want to analyze the results more deeply.*"
    ]
   },
   {
@@ -412,9 +414,13 @@
     "\n",
     "output_file = glob.glob(\"expert_distribution_*.csv\")[0]\n",
     "with open(output_file, \"r\") as f:\n",
-    "    print_highlight(\"Content of dumped record:\")\n",
-    "    for line in f:\n",
-    "        print_highlight(line.strip())"
+    "    print_highlight(\"\\n| Layer ID | Expert ID | Count |\")\n",
+    "    print_highlight(\"|----------|-----------|--------|\")\n",
+    "    next(f)\n",
+    "    for i, line in enumerate(f):\n",
+    "        if i < 9:\n",
+    "            layer_id, expert_id, count = line.strip().split(\",\")\n",
+    "            print_highlight(f\"| {layer_id:8} | {expert_id:9} | {count:6} |\")"
    ]
   },
   {