diff --git a/doc/source/data/examples/big_data_ingestion.ipynb b/doc/source/data/examples/big_data_ingestion.ipynb
index 279da331d..967ce33f5 100644
--- a/doc/source/data/examples/big_data_ingestion.ipynb
+++ b/doc/source/data/examples/big_data_ingestion.ipynb
@@ -362,7 +362,7 @@
     "# -> throughput: 8.56GiB/s\n",
     "```\n",
     "\n",
-    "Note: The pipeline can also be submitted using [Ray Job Submission](https://docs.ray.io/en/latest/cluster/job-submission.html) ,\n",
+    "Note: The pipeline can also be submitted using [Ray Job Submission](https://docs.ray.io/en/latest/cluster/running-applications/job-submission/) ,\n",
     "which is in beta starting with Ray 1.12.  Try it out!"
    ]
   }
diff --git a/doc/source/ray-air/examples/huggingface_text_classification.ipynb b/doc/source/ray-air/examples/huggingface_text_classification.ipynb
index f4a719c82..6ba93c934 100644
--- a/doc/source/ray-air/examples/huggingface_text_classification.ipynb
+++ b/doc/source/ray-air/examples/huggingface_text_classification.ipynb
@@ -61,7 +61,7 @@
       "source": [
         "We will use `ray.init()` to initialize a local cluster. By default, this cluster will be compromised of only the machine you are running this notebook on. You can also run this notebook on an Anyscale cluster.\n",
         "\n",
-        "This notebook *will not* run in [Ray Client](https://docs.ray.io/en/latest/cluster/ray-client.html) mode."
+        "This notebook *will not* run in [Ray Client](https://docs.ray.io/en/latest/cluster/running-applications/job-submission/ray-client.html) mode."
       ]
     },
     {
diff --git a/doc/source/ray-air/examples/tfx_tabular_train_to_serve.ipynb b/doc/source/ray-air/examples/tfx_tabular_train_to_serve.ipynb
index e4cefdee3..3a1ff794f 100644
--- a/doc/source/ray-air/examples/tfx_tabular_train_to_serve.ipynb
+++ b/doc/source/ray-air/examples/tfx_tabular_train_to_serve.ipynb
@@ -784,7 +784,7 @@
     "id": "OlzjlW8QR_q6"
    },
    "source": [
-    "We will use Ray Serve to serve the trained model. A core concept of Ray Serve is [Deployment](https://docs.ray.io/en/latest/serve/core-apis.html). It allows you to define and update your business logic or models that will handle incoming requests as well as how this is exposed over HTTP or in Python.\n",
+    "We will use Ray Serve to serve the trained model. A core concept of Ray Serve is [Deployment](https://docs.ray.io/en/latest/serve/getting_started.html#converting-to-a-ray-serve-deployment). It allows you to define and update your business logic or models that will handle incoming requests as well as how this is exposed over HTTP or in Python.\n",
     "\n",
     "In the case of serving model, `ray.serve.air_integrations.Predictor` and `ray.serve.air_integrations.PredictorDeployment` wrap a `ray.air.checkpoint.Checkpoint` into a Ray Serve deployment that can readily serve HTTP requests.\n",
     "Note, ``Checkpoint`` captures both model and preprocessing steps in a way compatible with Ray Serve and ensures that ml workload can transition seamlessly between training and\n",
diff --git a/python/ray/client_builder.py b/python/ray/client_builder.py
index cecb21ce9..bfb831cac 100644
--- a/python/ray/client_builder.py
+++ b/python/ray/client_builder.py
@@ -22,7 +22,10 @@ from ray.widgets import Template
 
 logger = logging.getLogger(__name__)
 
-CLIENT_DOCS_URL = "https://docs.ray.io/en/latest/cluster/ray-client.html"
+CLIENT_DOCS_URL = (
+    "https://docs.ray.io/en/latest/cluster/running-applications/"
+    "job-submission/ray-client.html"
+)
 
 
 @dataclass