diff --git a/docs/docs/integrations/vectorstores/azuresearch.ipynb b/docs/docs/integrations/vectorstores/azuresearch.ipynb
index 4298130f2bfd16..600ac3465d1e2c 100644
--- a/docs/docs/integrations/vectorstores/azuresearch.ipynb
+++ b/docs/docs/integrations/vectorstores/azuresearch.ipynb
@@ -8,8 +8,7 @@
    "source": [
     "# Azure AI Search\n",
     "\n",
-    "[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) (formerly known as `Azure Search` and `Azure Cognitive Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.\n",
-    "\n"
+    "[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) (formerly known as `Azure Search` and `Azure Cognitive Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale.\n"
    ]
   },
   {
@@ -292,7 +291,7 @@
    "source": [
     "## Perform a vector similarity search with relevance scores\n",
     " \n",
-    "Execute a pure vector similarity search using the similarity_search_with_relevance_scores() method:"
+    "Execute a pure vector similarity search using the similarity_search_with_relevance_scores() method. Queries that don't meet the threshold requirements are exluded."
    ]
   },
   {
@@ -331,9 +330,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Perform a Hybrid Search\n",
+    "## Perform a hybrid search\n",
     "\n",
-    "Execute hybrid search using the search_type or hybrid_search() method:"
+    "Execute hybrid search using the search_type or hybrid_search() method. Vector and nonvector text fields are queried in parallel, results are merged, and top matches of the unified result set are returned."
    ]
   },
   {
@@ -356,7 +355,7 @@
     }
    ],
    "source": [
-    "# Perform a hybrid search\n",
+    "# Perform a hybrid search using the search_type parameter\n",
     "docs = vector_store.similarity_search(\n",
     "    query=\"What did the president say about Ketanji Brown Jackson\",\n",
     "    k=3,\n",
@@ -385,7 +384,7 @@
     }
    ],
    "source": [
-    "# Perform a hybrid search\n",
+    "# Perform a hybrid search using the hybrid_search method\n",
     "docs = vector_store.hybrid_search(\n",
     "    query=\"What did the president say about Ketanji Brown Jackson\", k=3\n",
     ")\n",
@@ -396,7 +395,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Create a new index with custom filterable fields \n",
+    "## Custom schemas and queries\n",
+    "\n",
+    "This section shows you how to replace the default schema with a custom schema.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a new index with custom filterable fields \n",
     "\n",
     "This schema shows field definitions. It's the default schema, plus several new fields attributed as filterable. Because it's using the default vector configuration, you won't see vector configuration or vector profile overrides here. The name of the default vector profile is \"myHnswProfile\" and it's using a vector configuration of Hierarchical Navigable Small World (HNSW) for indexing and queries against the content_vector field.\n",
     "\n",
@@ -566,7 +574,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Create a new index with a Scoring Profile\n",
+    "### Create a new index with a scoring profile\n",
     "\n",
     "Here's another custom schema that includes a scoring profile definition. A scoring profile is used for relevance tuning of nonvector content, which is helpful in hybrid search scenarios."
    ]