diff --git a/python/ray/runtime_env/runtime_env.py b/python/ray/runtime_env/runtime_env.py
index 5a19c6b1436f..b56b37073f0f 100644
--- a/python/ray/runtime_env/runtime_env.py
+++ b/python/ray/runtime_env/runtime_env.py
@@ -180,21 +180,21 @@ class RuntimeEnv(dict):
     .. code-block:: python
 
         from ray.runtime_env import RuntimeEnv
-        # Invoke a remote task that will run in a specified runtime environment.
+        # Invoke a remote task that runs in a specified runtime environment.
         f.options(runtime_env=RuntimeEnv(...)).remote()
 
-        # Instantiate an actor that will run in a specified runtime environment.
+        # Instantiate an actor that runs in a specified runtime environment.
         actor = SomeClass.options(runtime_env=RuntimeEnv(...)).remote()
 
         # Specify a runtime environment in the task definition. Future invocations via
-        # `g.remote()` will use this runtime environment unless overridden by using
+        # `g.remote()` use this runtime environment unless overridden by using
         # `.options()` as above.
         @ray.remote(runtime_env=RuntimeEnv(...))
         def g():
             pass
 
         # Specify a runtime environment in the actor definition. Future instantiations
-        # via `MyClass.remote()` will use this runtime environment unless overridden by
+        # via `MyClass.remote()` use this runtime environment unless overridden by
         # using `.options()` as above.
         @ray.remote(runtime_env=RuntimeEnv(...))
         class MyClass:
@@ -222,36 +222,41 @@ class MyClass:
             pip={"packages":["tensorflow", "requests"], "pip_check": False,
             "pip_version": "==22.0.2;python_version=='3.8.11'"})
 
+        # Example for using image_uri
+        RuntimeEnv(
+            image_uri="rayproject/ray:2.39.0-py312-cu123")
+
     Args:
         py_modules: List of URIs (either in the GCS or external
-            storage), each of which is a zip file that will be unpacked and
-            inserted into the PYTHONPATH of the workers.
+            storage), each of which is a zip file that Ray unpacks and
+            inserts into the PYTHONPATH of the workers.
         working_dir: URI (either in the GCS or external storage) of a zip
-            file that will be unpacked in the directory of each task/actor.
+            file that Ray unpacks in the directory of each task/actor.
         pip: Either a list of pip packages, a string
-            containing the path to a pip requirements.txt file, or a python
+            containing the path to a pip requirements.txt file, or a Python
             dictionary that has three fields: 1) ``packages`` (required, List[str]): a
             list of pip packages, 2) ``pip_check`` (optional, bool): whether enable
             pip check at the end of pip install, defaults to False.
-            3) ``pip_version`` (optional, str): the version of pip, Ray will spell
+            3) ``pip_version`` (optional, str): the version of pip, Ray prepends
             the package name "pip" in front of the ``pip_version`` to form the final
             requirement string, the syntax of a requirement specifier is defined in
             full in PEP 508.
-        uv: Either a list of pip packages, or a python dictionary that has one field:
+        uv: Either a list of pip packages, or a Python dictionary that has one field:
             1) ``packages`` (required, List[str]).
         conda: Either the conda YAML config, the name of a
             local conda env (e.g., "pytorch_p36"), or the path to a conda
             environment.yaml file.
-            The Ray dependency will be automatically injected into the conda
-            env to ensure compatibility with the cluster Ray. The conda name
-            may be mangled automatically to avoid conflicts between runtime
-            envs.
-            This field cannot be specified at the same time as the 'pip' field.
-            To use pip with conda, please specify your pip dependencies within
+            Ray automatically injects the dependency into the conda
+            env to ensure compatibility with the cluster Ray. Ray may automatically
+            mangle the conda name to avoid conflicts between runtime envs.
+            This field can't be specified at the same time as the 'pip' field.
+            To use pip with conda, specify your pip dependencies within
             the conda YAML config:
             https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually
         container: Require a given (Docker) container image,
-            The Ray worker process will run in a container with this image.
+            The Ray worker process runs in a container with this image.
+            This parameter only works alone, or with the ``config`` or
+            ``env_vars`` parameters.
             The `run_options` list spec is here:
             https://docs.docker.com/engine/reference/run/
         env_vars: Environment variables to set.
@@ -266,6 +271,9 @@ class MyClass:
         config: config for runtime environment. Either
             a dict or a RuntimeEnvConfig. Field: (1) setup_timeout_seconds, the
             timeout of runtime environment creation,  timeout is in seconds.
+        image_uri: URI to a container image. The Ray worker process runs
+            in a container with this image. This parameter only works alone,
+            or with the ``config`` or ``env_vars`` parameters.
     """
 
     known_fields: Set[str] = {