diff --git a/docs/backend/constrained_decoding.ipynb b/docs/backend/constrained_decoding.ipynb
index a0ea915f3..88cf4a717 100644
--- a/docs/backend/constrained_decoding.ipynb
+++ b/docs/backend/constrained_decoding.ipynb
@@ -4,17 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Constrained Decoding Tutorial\n",
-    "\n",
-    "This tutorial shows how to format model outputs using constrained decoding in SGLang."
+    "# Constrained Decoding"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Constrained Decoding\n",
-    "\n",
     "With SGLang, You can define a JSON schema, EBNF or regular expression to constrain the model's output.\n",
     "\n",
     "[JSON Schema](https://json-schema.org/): Formats output into structured JSON objects with validation rules.\n",
@@ -23,7 +19,7 @@
     "\n",
     "[Regular Expressions](https://en.wikipedia.org/wiki/Regular_expression): Matches text patterns for simple validation and formatting.\n",
     "\n",
-    "### Constrained Decoding Backends\n",
+    "## Grammar Backend\n",
     "\n",
     "SGLang has two backends: [Outlines](https://github.com/dottxt-ai/outlines) (default) and [XGrammar](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar). We suggest using XGrammar whenever possible for its better performance. For more details, see [XGrammar technical overview](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar).\n",
     "\n",